# Helium Variation Across Two Solar Cycles

B. L. Alterman University of Michigan
Department of Applied Physics
450 Church St.
Ann Arbor, MI 48109, USA  University of Michigan
Department of Climate & Space Sciences & Engineering
2455 Hayward St.
Ann Arbor, MI 48109-2143, USA  Justin C. Kasper University of Michigan
Department of Climate & Space Sciences & Engineering
2455 Hayward St.
Ann Arbor, MI 48109-2143, USA  Smithsonian Astrophysical Observatory
Observatory Building E
60 Garden St.
Cambridge, MA 02138, USA

###### Abstract

We study the relationship between solar wind helium to hydrogen abundance
ratio ($\mathrm{A}_{\mathrm{He}}$), and sunspot number ($\mathrm{SSN}$) over
solar cycles 23 and 24. This is the first full 22-year Hale cycle measured
with the Wind spacecraft covering a full cycle of the solar dynamo with two
polarity reversals. While previous studies have established a strong
correlation between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$, we show
that the phase delay between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ is
a monotonic increasing function of $v_{\mathrm{sw}}$. Correcting for this lag,
$\mathrm{A}_{\mathrm{He}}$ returns to the same $\mathrm{SSN}$ over all rising
and falling phases and across solar wind speeds.

solar wind, sunspots, Sun: abundances, acceleration of particles,
interplanetary medium, Sun: fundamental parameters

††software:  IPython (Perez & Granger, 2007), Jupyter (Kluyver et al., 2016),
Matplotlib (Hunter, 2007), Numpy (van der Walt et al., 2011), Pandas
(Mckinney, 2010), Python (Oliphant, 2007; Millman & Aivazis, 2011)

Published

##  1 Introduction

Figure 1: Helium abundance ($\mathrm{A}_{\mathrm{He}}$) as a function of time
and solar wind speed. Solar wind speed ($v_{\mathrm{sw}}$) is divided into ten
quantiles. Thirteen month smoothed SIDC Sunspot Number (SSN, dashed black) is
plotted on the secondary y-axis. The legend indicates the middle of a given
$v_{\mathrm{sw}}$ quantile and the Spearman rank correlation coefficient
between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ for that quantile. In
effect, this figure updates Fig. (1) of Kasper et al. (2007, 2012). The
present drop in $\mathrm{A}_{\mathrm{He}}$ reflects the onset of solar minimum
25.

Fully ionized hydrogen or protons ($p$) and fully ionized helium or alpha
particles ($\mathrm{He}$ or $\alpha$) are the two most abundant solar wind ion
species. The former comprises $\sim 95\%$ of the solar wind ions and the later
$\sim 4\%$, both by number density. Heavier, minor ions constitute the
remaining. The alpha particle abundance ($\mathrm{A}_{\mathrm{He}}=100\times
n_{\alpha}/n_{p}$) strongly correlates with solar activity, as indicated by
the sunspot number ($\mathrm{SSN}$) (Aellig et al., 2001; Kasper et al., 2007,
2012). The cross correlation and slope between $\mathrm{A}_{\mathrm{He}}$ and
$\mathrm{SSN}$ varies with solar wind speed ($v_{\mathrm{sw}}$); is strongest
in slow wind; markedly falls off above
$v_{\mathrm{sw}}=426\;\mathrm{km\,s^{-1}}$, where $\mathrm{A}_{\mathrm{He}}$
takes on a stable value between $4\%$ and $5\%$; and vanishes in the solar
wind for speeds below $v_{0}=259\;\mathrm{km\,s^{-1}}$ (Kasper et al., 2007,
2012). This helium vanishing speed is within $1\;\sigma$ of the minimum
observed solar wind speed (Kasper et al., 2007), indicating that helium may be
essential to solar wind formation in the corona.

In addition to $\mathrm{SSN}$, many other indicators of solar activity also
follow a similar $\sim 11$ year cycle (Ramesh & Vasantharaju, 2014) that
demonstrate a distinct phase-offset with $\mathrm{SSN}$, which has been
referred to as a hysteresis-like effect. Goelzer et al. (2013) have shown a
similar phase lag in the interplanetary magnetic field’s response to changes
in $\mathrm{SSN}$.

Using observations from the Wind Faraday cups (FC), we extend the study of
$\mathrm{A}_{\mathrm{He}}$ variation with $\mathrm{SSN}$ and $v_{\mathrm{sw}}$
by Kasper et al. (2007, 2012) to include more than 23 years. This time period
encompasses solar cycles 23 and 24 along with the end of solar cycle 22,
thereby covering one Hale cycle. In other words, an idealized sun with a pure
dipole magnetic field would be returning to the configuration it had at the
end of cycle 22.

In this work, we expand on the results of Kasper et al. (2007, 2012). We show
a positive correlation between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$
across multiple solar cycles. In the slowest wind, we find a characteristic
$\mathrm{A}_{\mathrm{He}}$ that is consistent across multiple minima and
maxima. Examining this relationship over one Hale cycle, we demonstrate that
the phase lag between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ found by
Feldman et al. (1978) is a monotonically increasing function of
$v_{\mathrm{sw}}$. This delay is characteristic to a given $v_{\mathrm{sw}}$
and, at any one $v_{\mathrm{sw}}$, a cyclic delay is sufficient to correct for
this lag. Our results are consistent when using the 13-month smoothed,
monthly, and daily sunspot numbers.

The remainder of this Letter is dedicated to analyzing and interpreting this
speed-dependent lag. Section 2 describes the observations and FC specifics
that are key to this study. Section 3 describes the variation of
$\mathrm{A}_{\mathrm{He}}$ with $v_{\mathrm{sw}}$ and $\mathrm{SSN}$ over two
solar cycles. Section 4 analyzes the delay in response of
$\mathrm{A}_{\mathrm{He}}$ to changes in $\mathrm{SSN}$ as a function of
$v_{\mathrm{sw}}$. Section 5 presents the relationship between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ in various $v_{\mathrm{sw}}$
quantiles, corrected for the delay of peak cross-correlation coefficient.
Here, we show that correcting for the lag in $\mathrm{A}_{\mathrm{He}}$’s
response to changes in $\mathrm{SSN}$ reduces this hysteresis effect to a
linear relationship. In Section 6, we use $\mathrm{A}_{\mathrm{He}}$’s
dependence on $\mathrm{SSN}$ to investigate the robustness of the
$\mathrm{A}_{\mathrm{He}}$, $v_{\mathrm{sw}}$, $\mathrm{SSN}$ relationship
reported by Kasper et al. (2007). Section 7, we interpret our results and
extend earlier hypotheses regarding two sources of slow solar wind. Finally,
Section 8 summarizes these results and discusses future work.

##  2 Data Sources

The Wind spacecraft has been in continuous operation since its launch in the
fall of 1994. Ogilvie et al. (1995) provide a detailed description of the
Solar Wind Experiment (SWE) Faraday cups (FC). Kasper et al. (2006) introduce
techniques for optimizing the algorithms that extract physical quantities from
FC measurements. Maruca & Kasper (2013) and Alterman et al. (2018) build on
these algorithms. These data have resulted high precision measurements of
alpha particles (Kasper et al., 2006; Maruca & Kasper, 2013) and multiple
proton populations (Alterman et al., 2018). The FC ion distributions are
available on CDAweb111https://cdaweb.sci.gsfc.nasa.gov/misc/NotesW.html#WI_SW-
ION-DIST_SWE-FARADAY and
SPDF222ftp://spdf.gsfc.nasa.gov/pub/data/wind/swe/swe_faraday/. We follow
Alterman et al. (2018) and reprocess the raw measurements to extract two
proton populations (core and beam) along with an alpha particle population.
The proton core is the population with the larger of the two proton densities.
We calculate the solar wind speed as the proton center-of-mass velocity and
treat the proton core as the proton density when calculating
$\mathrm{A}_{\mathrm{He}}$.

Two aspects of FCs are key to this work. First, FCs are energy-per-charge
detectors. In the highly supersonic solar wind, alpha particles and protons
are well separated by the instrument even when they are co-moving (Kasper et
al., 2008, 2017; Alterman et al., 2018), . Second, the measurement quality has
been stable and accurate throughout the mission (Kasper et al., 2006). These
two FC characteristics enable our study of $\mathrm{A}_{\mathrm{He}}$
variation with a single dataset from one instrument suite covering the 23
years necessary to observe one Hale cycle.

##  3 Solar Cycle Variation

Fig. 1 presents $\mathrm{A}_{\mathrm{He}}$ as a function of $v_{\mathrm{sw}}$
and time over 23 years. This time period starts at the trailing end of cycle
22 and extends through the declining phase of cycle 24. Fig. 1 follows the
format of Kasper et al. (2007, 2012, Figure (1) in each) and can be considered
an update to their results. The solar wind speed measurements from the full
mission have been split into 12 quantiles. The fastest and slowest quantile
have been discarded due to measurement and statistical considerations. Of
those quantiles retained, the lower edge of the slowest is
$312\;\mathrm{km\,s^{-1}}$ and the upper edge of the fastest is
$574\;\mathrm{km\,s^{-1}}$. Consequently, this study is limited to solar wind
typically categorized as slow or slow and intermediate speed.333To be
consistent with prior work (e.g. Kasper et al. (2007, 2012)), we will use
_slow_ and _fast_ to refer to the different extremes presented here. However,
the reader should known that truly fast solar wind is excluded from our study.
As in prior work, the abundance in each $v_{\mathrm{sw}}$ quantile is averaged
into 250 day intervals. The 13-month smoothed sunspot number (SILSO World Data
Center, 2018; Vanlommel et al., 2005, $\mathrm{SSN}$) is interpolated to the
measurement time; averaged into the same 250 day intervals as
$\mathrm{A}_{\mathrm{He}}$; and plotted on the secondary y-axis. The legend
indicates the middle of the solar wind speed quantile along with its
corresponding Spearman rank cross correlation coefficient between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$. For brevity, we henceforth
indicate the Spearman rank cross-correlation coefficient between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ as
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$.

Fig. 1 indicates that $\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ peaks at
$v_{\mathrm{sw}}=355\;\mathrm{km\,s^{-1}}$. The present drop in
$\mathrm{A}_{\mathrm{He}}$ reflects that the sun is entering Minimum 25. In
contrast to the results of Kasper et al. (2007, 2012),
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})>0.6$ indicates a meaningful
cross-correlation in all but the fastest reported quantile with
$v_{\mathrm{sw}}=542\;\mathrm{km\,s^{-1}}$ and
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})\ \geq 0.7$ is highly significant
up to $v_{\mathrm{sw}}=426\;\mathrm{km\,s^{-1}}$. there is a phase offset
between $\mathrm{A}_{\mathrm{He}}$ and
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$.

##  4 Time-Lagged Cross Correlation

Visual inspection indicates a clear time lag between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$. To quantify this lag, we
calculate $\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ as a function of delay
time applied to $\mathrm{SSN}$ from $-200\;\mathrm{days}$ to
$+600\;\mathrm{days}$ in steps of 40 days–slightly longer than one solar
rotation–for each $v_{\mathrm{sw}}$ quantile. We smooth these results to
reduce the impact of discretization. The delay time is the time for which
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ peaks as a function of delay.
Fig. 2 plots the peak cross correlation coefficient as a function of
$v_{\mathrm{sw}}$ for observed (empty marker) and delayed (filled marker)
$\mathrm{SSN}$. Marker colors and symbols match Fig. 1 and are maintained
throughout the Letter. Dotted lines connect the markers to aid the eye. To
estimate the error in this calculation and its sensitivity to averaging
timescale, we repeated it for averaging windows $N_{\mathrm{days}}=225$ to
$N_{\mathrm{days}}=275$ in steps of 5 days. Because a trend is not apparent,
we choose to quantify this variability as the standard deviation across
$N_{\mathrm{days}}$ and represent it as error bars centered on the
$N_{\mathrm{days}}=250$ averaging window utilized in this Letter.

Several features in Fig. 2 stand out. First, it emphasizes that delayed
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})\geq 0.7$ is highly correlated for
all $v_{\mathrm{sw}}$ quantiles. Second, observed and delayed
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ peak at the same
$v_{\mathrm{sw}}=355\;\mathrm{km\,s^{-1}}$. Third, the change in
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ is largest and most visually
striking in faster wind. However, smaller changes in slower wind’s
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ are statistically more
significant because they are less likely to be due to random fluctuations.

Figure 2:  Plots characterizing the cross correlation coefficient as a
function of solar wind speed ($v_{\mathrm{sw}}$) for the observed (empty
markers) and delayed (filled markers) $\mathrm{SSN}$ using 250 day averages.
The marker color and shape match the style of Fig. 1. Dotted lines connect the
markers to aid the eye. Panels are: (a) Spearman rank cross-correlation
coefficient and (b) Delay of Peak Spearman rank cross correlation as a
function of $v_{\mathrm{sw}}$. In (b), the dashed green line indicates a
robust fit and the panel’s insert provides the functional form, fit
parameters, and quality metrics. A positive delay indicates that changes in
$\mathrm{SSN}$ precede changes in $\mathrm{A}_{\mathrm{He}}$.

Fig. 2 examines $\tau$, the delay of peak
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$, as a function of
$v_{\mathrm{sw}}$. The insert at the top of the figure indicates the
functional form, fit parameters, and quality metrics. As with Fig. 2, the
error bars indicate the variability of $\tau$ in each $v_{\mathrm{sw}}$
quantile. Solving the fit equation for $\tau=0$, or the speed at which
$\mathrm{A}_{\mathrm{He}}$ responds immediately to changes in $\mathrm{SSN}$,
results in $v_{\mathrm{i}}=200\;\mathrm{km\,s^{-1}}$. Nevertheless, it is not
unambiguously clear if delay time $\tau$ monotonically increases with
$v_{\mathrm{sw}}$ or there are two distinct delay times. If it is actually the
latter, then $\mathrm{A}_{\mathrm{He}}$ in slow wind responds to changes in
$\mathrm{SSN}$ with a delay time $\tau_{\mathrm{slow}}=150\;\mathrm{days}$;
faster wind responds after $\tau_{\mathrm{fast}}>300\;\mathrm{days}$; and
$v_{\mathrm{i}}$ represents a non-trivial conflation of these two delays. If
this is not the case, it may be that $\tau_{\mathrm{slow}}$ is the shortest
delay with which $\mathrm{A}_{\mathrm{He}}$ responds to changes in
$\mathrm{SSN}$. all helium released into the solar wind still lags changes in
$\mathrm{SSN}$.

##  5 Phase Delay

Fig. 3 presents $\mathrm{A}_{\mathrm{He}}$ as a function of $\mathrm{SSN}$ in
the example quantile $v_{\mathrm{sw}}=355\;\mathrm{km\,s^{-1}}$. This is the
$v_{\mathrm{sw}}$ quantile for which the change in cross-correlation
coefficient $\Delta\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ is smallest
and the phase delay’s effect is least likely to be due to random fluctuations.
Panel (a) uses the observed $\mathrm{SSN}$. Panel (b) uses $\mathrm{SSN}$
delayed by the time indicated in Fig. 2, $\sim 150\;\mathrm{days}$. A line
connects the points to aid the eye. Both line and marker color indicate the
days since mission start, given by the color bars. Both panels contain a
robust fit to the data, each indicating the monotonic, increasing trend. As in
Fig. 2, the insert at the top of each panel describes the fit.

Panel (a) clearly shows the hysteresis pattern of $\mathrm{A}_{\mathrm{He}}$
as a function of $\mathrm{SSN}$. As seen with other indices (e.g. Bachmann &
White (1994)), time moves counter-clockwise in this plot.444Not all indices
present with the same handedness and the handedness of some changes across
solar cycles (Özgüç & Ataç, 2001). A larger study of
$\mathrm{A}_{\mathrm{He}}$ variation is necessary to generalize this
handedness observation. As noted by Bachmann & White (1994) for several solar
indices, the clustering of data at small $\mathrm{SSN}$ indicates that the
hysteresis effect is stronger at solar maximum and weaker at solar minimum.

In panel (b), the larger $R^{2}$ indicates that this spread of
$\mathrm{A}_{\mathrm{He}}$ about the trend decreases. Note that $R^{2}$
corresponds to the square of the correlation coefficient of
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ derived from a robust fit and
not directly from the measurements. _Although $R$ is similar to
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$, they are not trivially equal._
That delayed $\chi^{2}_{\nu}$ is markedly closer to unity indicates that a
linear model better characterizes $\mathrm{A}_{\mathrm{He}}$ as a function of
delayed than observed $\mathrm{SSN}$. Because delayed $\mathrm{SSN}$ only
reduces the spread of $\mathrm{A}_{\mathrm{He}}$ about the trend, it is
expected that the trends in both cases are similar.

Figure 3:  Helium abundance ($\mathrm{A}_{\mathrm{He}}$) as a function of (a)
observed and (b) delayed $\mathrm{SSN}$ in one example $v_{\mathrm{sw}}$
quantile. A line connects the points to aid the eye. Line and marker color
correspond to the number of days since mission start. This $v_{\mathrm{sw}}$
quantile covers the range $347\;\mathrm{km\,s^{-1}}<v_{\mathrm{sw}}\leq
363\;\mathrm{km\,s^{-1}}$. A green, dashed line presents a robust fit to each
trend. The insert at top of each panel gives the function fit, fit parameters,
and quality metrics. Delaying $\mathrm{SSN}$ by the phase offset appropriate
to this $v_{\mathrm{sw}}$ quantile reduces the impact of the hysteresis
effect, as the increase in delayed $R^{2}$ indicates.

##  6 Robustness of $\mathrm{A}_{\mathrm{He}}(v)$

Kasper et al. (2007) describe the relationship between
$\mathrm{A}_{\mathrm{He}}$ and $v_{\mathrm{sw}}$ in slow wind
($v_{\mathrm{sw}}\leq 530\;\mathrm{km\,s^{-1}}$) using data from a 2 year
interval surrounding solar Minimum 23. They find that
$\mathrm{A}_{\mathrm{He}}(v)=1.63\times 10^{-2}\,(v-v_{0})$, where
$v_{0}=259\pm 12\;\mathrm{km\,s^{-1}}$ is the speed below which helium
vanishes from the solar wind. The robust fits in Fig. 3 allow us to extract
$\mathrm{A}_{\mathrm{He}}$ at zero solar activity for all $v_{\mathrm{sw}}$
quantiles. This quantity, $\mathrm{A}_{\mathrm{He}}(\mathrm{SSN}=0)$,
represents low solar activity conditions across this Hale cycle that are
appropriate for comparison to the minimum 23 results from Kasper et al.
(2007).

Fig. 4 plots $\mathrm{A}_{\mathrm{He}}(\mathrm{SSN}=0)$ in all
$v_{\mathrm{sw}}$ quantiles for delayed $\mathrm{SSN}$ with unfilled markers.
As observed $\mathrm{SSN}$ does not deviate from delayed $\mathrm{SSN}$ in
this figure, it is omitted for clarity. The black dashed curve is the fit of
$\mathrm{A}_{\mathrm{He}}(v)$ from Kasper et al. (2007). To better compare
this analysis to the work of Kasper et al. (2007), filled markers present the
results of repeating this analysis for $\mathrm{SSN}<25$, a range in
$\mathrm{SSN}$ representative of solar minimum 23. That
$\mathrm{A}_{\mathrm{He}}(\mathrm{SSN}=0)$ is smaller in this reanalysis using
a restricted range of $\mathrm{SSN}$ further substantiates that our results
are consistent with those of Kasper et al. (2007) even though ours cover
multiple solar cycles, a larger range in solar activity conditions, . The
discrepancy between our fastest quantile with
$v_{\mathrm{sw}}=542\;\mathrm{km\,s^{-1}}$ and their trend is expected because
(1) it is outside of the speed range they fit and (2) they found thet
$\mathrm{A}_{\mathrm{He}}$ at this and similarly high speeds takes on a stable
value between $4\%$ and $5\%$.

Figure 4: A summary of the zero solar activity helium abundance,
$\mathrm{A}_{\mathrm{He}}(\mathrm{SSN}=0)$, as a function of $v_{\mathrm{sw}}$
for all robust fits in the fashion of Fig. 3. Error bars indicate the standard
deviation of each quantity over the range in averaging windows $225\leq
N_{\mathrm{days}}\leq 275$ , each centered on the $N_{\mathrm{days}}=250$
value. Unfilled markers show all $\mathrm{SSN}$. Filled markers show identical
calculations with $\mathrm{SSN}<25$. The black dashed curve is the
relationship between $\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ derived by
Kasper et al. (2007). That repeating our calculation with a reduced range in
$\mathrm{SSN}$ shows better agreement with the results of Kasper et al. (2007)
indicates that our results, covering the full range of solar activity in
cycles 23 and 24, are consistent with their results from the two year interval
surrounding minimum 23.

##  7 Helium Filtration

Many solar indices have a distinct phase-offset or hysteresis-like behavior
with $\mathrm{SSN}$ (Ramesh & Vasantharaju, 2014, and references therein). Two
such indicators include Lyman-$\alpha$ ($\mathrm{L}\alpha$) intensity and soft
x-ray flux ($\mathrm{SXR}$). $\mathrm{L}\alpha$ measures activity in the sun’s
chromosphere & transition region (Fontenla et al., 2001, 1988) and lags
$\mathrm{SSN}$ by 125 days (Bachmann & White, 1994). $\mathrm{SXR}$ is most
common in Active Regions (AR) (van Driel-Gesztelyi & Green, 2015), and lags
$\mathrm{SSN}$ by 300 days to 450 days (Temmer et al., 2003).

While $\mathrm{A}_{\mathrm{He}}$ is approximately $8.5\%$ within the sun’s
convection zone and out to the photosphere (Asplund et al., 2009; Laming,
2015), it rarely exceeds $5\%$ in the corona (Laming & Feldman, 2003; Mauas et
al., 2005). It has long been assumed that $\mathrm{A}_{\mathrm{He}}$ is
initially modified in the photosphere. However, the speed-dependent lag in
$\mathrm{A}_{\mathrm{He}}$’s response to changes in $\mathrm{SSN}$ found here
suggests additional processes at higher altitudes further modify helium’s
abundance. Slow solar wind’s 150 day lag tracks lags in transition region and
chromosphere structures, while faster wind’s 300 day lag is more consistent
with higher altitude structures in the corona. How could the transition region
or corona modify the helium abundance?

Kasper et al. (2007) propose that two mechanisms release fully ionized helium
into the slow solar wind. ARs have a strong magnetic field that extends from
the photosphere into the corona, originate well above the equatorial region,
tend to migrate towards the equator as they get older, and have loops that
tend to grow with age (van Driel-Gesztelyi & Green, 2015). In contrast, the
streamer belt has a weaker magnetic field, is composed of loops larger than
those typical of ARs, is magnetically closed to the heliosphere, and is
typically considered the source of slow solar wind (Eselevich & Eselevich,
2006). Stakhiv et al. (2016) identify signatures of these two solar sources in
ACE/SWICS composition measurements.

If there are two sources of slow wind, solar wind originating in the streamer
belt is more processed than that originating in ARs, where $\mathrm{SXR}$ is
enhanced. Slower wind $\mathrm{A}_{\mathrm{He}}$
($v_{\mathrm{sw}}<375\;\mathrm{km\,s^{-1}}$) originates from the streamer belt
with a phase delay $\tau_{\mathrm{slow}}=150\;\mathrm{days}$. It appears more
depleted than faster solar wind from ARs that has a phase delay
$\tau_{\mathrm{fast}}>300\;\mathrm{days}$. The magnitude of
$\mathrm{A}_{\mathrm{He}}$’s reduction from its photospheric value and the
speed-dependent delay then reflect the extent to which a given source region
is magnetically open to the heliosphere. As the phase delay between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$ is an increasing function of
$v_{\mathrm{sw}}$, ARs and the streamer belt may be two extreme cases along
the continuum of slow wind helium depletion mechanisms.

For illustrative purposes, one candidate mechanism that may contribute to this
processing is the FIP effect. The FIP effect is the empirical observation that
solar wind ion are fractionated, or their abundances differ from their
photospheric value based on their first ionization potential (Meyer, 1991,
1993; Laming, 2015, and references therein). The FIP effect is strongest in
the upper chromosphere and lower transition region, weakest in regions of
strong magnetic field, stronger in longer loops (Rakowski & Laming, 2012).

However, this is just one of several possible mechanisms that could cause this
phase lag. Other mechanisms that might impact the speed-dependent phase lag
may include interchange reconnection (Fisk, 2003) and gravitational settling
(Hirshberg, 1973; Borrini et al., 1981; Vauclair & Charbonnel, 1991). Whatever
the underlying mechanism, it should also account for the observation that
$\mathrm{A}_{\mathrm{He}}$ returns to a similar value during solar maximum,
irrespective of $\mathrm{SSN}$ during maximum.

##  8 Conclusion

Following the methods of Kasper et al. (2007, 2012), we have analyzed the
relationship between $\mathrm{A}_{\mathrm{He}}$ and the 13-month smoothed
sunspot number ($\mathrm{SSN}$) by studying their cross correlation
coefficient using 250 day averages. We have verified that our results are
consistent when using the monthly and daily $\mathrm{SSN}$. Our data covers 23
years, including cycle 23 and 24 along with the tail end of cycle 22. This
time period is more than the 22 years of a Hale cycle over which the pure
dipole field of an idealized sun would return to an initial configuration. As
shown in Fig. 1, the present decrease in $\mathrm{A}_{\mathrm{He}}$ clearly
demonstrates that we are entering solar Minimum 25. While the significance of
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ decreases with increasing
$v_{\mathrm{sw}}$, Fig. 1 shows that
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ is meaningful up to
$v_{\mathrm{sw}}=488\;\mathrm{km\,s^{-1}}$ and highly significant up to
$v_{\mathrm{sw}}=426\;\mathrm{km\,s^{-1}}$.

Feldman et al. (1978) comment on a phase offset between
$\mathrm{A}_{\mathrm{He}}$ and $\mathrm{SSN}$. Panel (b) of Fig. 2 reveals
that (1) the length of this delay is an increasing function of
$v_{\mathrm{sw}}$ and (2) the $v_{\mathrm{sw}}$ quantile most correlated with
$\mathrm{SSN}$ does not change when $\mathrm{SSN}$ is appropriately delayed in
each quantile. We have also argued that, although changes in
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ are most dramatic in faster
$v_{\mathrm{sw}}$ quantiles, the probability of smaller changes in slower
wind’s larger $\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ is much smaller
and therefore more significant.

Fig. 2 presents the delay applied to $\mathrm{SSN}$ necessary to maximize
$\rho(\mathrm{A}_{\mathrm{He}},\mathrm{SSN})$ as a function of
$v_{\mathrm{sw}}$. linear fit to this trend reveals that the speed at which
$\mathrm{A}_{\mathrm{He}}$ responds instantaneously to changes in
$\mathrm{SSN}$ is $v_{\mathrm{i}}=200\;\mathrm{km\,s^{-1}}$. Yet the speed of
instantaneous response is less than the vanishing speed,
$v_{\mathrm{i}}<v_{0}$. Therefore any helium released into the solar wind will
necessarily response to changes in $\mathrm{SSN}$ after some delay. If trend
in Fig. 2 is correct, then the minimum delay in $\mathrm{A}_{\mathrm{He}}$’s
response to $\mathrm{SSN}$ is $68\pm 13\;\mathrm{days}$, or approximately two
Carrington Rotations. Here, we also note that there may be two distinct phase
delays ($\tau_{\mathrm{slow}}$ and $\tau_{\mathrm{fast}}$) with which
$\mathrm{A}_{\mathrm{He}}$ responds to changes in $\mathrm{SSN}$ and the fit
quantity $v_{\mathrm{i}}$ may be a conflation of the physics related to each
phase delay. Under either interpretation, helium released into the solar wind
is a delayed response to changes in $\mathrm{SSN}$.

In Section 5, we present robust fits to $\mathrm{A}_{\mathrm{He}}$ as a
function of observed and delayed $\mathrm{SSN}$ in each $v_{\mathrm{sw}}$
quantile. It visually illustrates that applying a time delay to $\mathrm{SSN}$
reduces the spread of $\mathrm{A}_{\mathrm{He}}$ about its trend. In Section
6, we use helium abundance at zero solar activity derived from these fits to
demonstrate that our results using 23 years of data are consistent with the
trend found by Kasper et al. (2007) for a two year interval surrounding solar
minimum 23.

In Section 7, we discuss how the demonstrated phase delay or hysteresis effect
is qualitatively similar to the phase delays between $\mathrm{SSN}$ and many
regularly observed solar indices (Ramesh & Vasantharaju, 2014, and references
therein). We note that the two aforementioned phase delays are consistent with
$\mathrm{L}\alpha$ and $\mathrm{SXR}$ and that this consistency is indicative
of two distinct source regions. Slower wind
($v_{\mathrm{sw}}<375\;\mathrm{km\,s^{-1}}$) with a lower
$\mathrm{A}_{\mathrm{He}}$ originates in the streamer belt and responds to
changes in $\mathrm{SSN}$ with characteristic delay time
$\tau_{\mathrm{slow}}=150\;\mathrm{days}$. Faster wind with a larger
$\mathrm{A}_{\mathrm{He}}$ originates in ARs and responds to changes in
$\mathrm{SSN}$ with characteristic delay time
$\tau_{\mathrm{fast}}>300\;\mathrm{days}$. Assuming that the results of Kasper
et al. (2007) apply across the solar cycle and helium vanishes from the solar
wind when $v_{\mathrm{sw}}<259\;\mathrm{km\,s^{-1}}$ , one possible
interpretation is that the mechanisms that reduces $\mathrm{A}_{\mathrm{He}}$
to a value less than its photospheric value prevents solar wind release below
the vanishing speed $v_{0}$, and–using the fit from Panel (b) of Fig. 2–any
helium that enters the solar wind is released after 68 days, approximately two
Carrington rotations. A rigorous study of the relationship between
$\mathrm{A}_{\mathrm{He}}$ and solar indices other than $\mathrm{SSN}$ may
better constrain helium variation by source region is a subject of future
work.

This work highlights the value of recent and forthcoming advances in
heliophysics. Parker Solar Probe (Fox et al., 2016, PSP) launched in August,
2018 and completed its first perihelion in November of that year. Solar
Orbiter (Müller et al., 2013, SolO) will launch in 2020. The instruments on
board (Kasper et al., 2016) provide an unprecedented opportunity to study the
solar wind, its formation, and its acceleration. For example, PSP will make
measurements near and below the Alfvén critical point, i.e. at distances
within which mapping the solar wind to specific sources is significantly
simplified in comparison with Wind. McMullin et al. (2016) anticipate that the
Daniel K. Inouye Solar Telescope (DKIST) will begin operations in 2020.
DKIST’s Cryo-NIRSP instrument will be capable of simultaneously imaging solar
helium at various heights in the corona. Combining DKIST measurements with PSP
and SolO measurements will enhance our ability to differentiate between the
mechanisms releasing helium into the solar wind–e.g. from the streamer belt or
ARs–and better constrain the delay in helium’s response to changes in
$\mathrm{SSN}$.

This work was funded by NASA grants NNX17AI18G and 80NSSC18K0986. We are
grateful to Michael Stevens for supplying the data and the referee for
insightful comments. We also thank Lennard Fisk, Enrico Landi, Liang Zhao,
Phyllis Whittlesey, and Michael Stevens for fruitful discussions.

## References

  * Aellig et al. (2001) Aellig, M. R., Lazarus, A. J., & Steinberg, J. T. 2001, Geophysical Research Letters, 28, 2767.  http://doi.wiley.com/10.1029/2000GL012771
  * Alterman et al. (2018) Alterman, B. L., Kasper, J. C., Stevens, M. L., & Koval, A. 2018, The Astrophysical Journal, 864, 112.  http://dx.doi.org/10.3847/1538-4357/aad23fhttp://stacks.iop.org/0004-637X/864/i=2/a=112?key=crossref.70d30c5e4f4e09560b242739d2b64fbe
  * Asplund et al. (2009) Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, Annual Review of Astronomy and Astrophysics, 47, 481
  * Bachmann & White (1994) Bachmann, K. T., & White, O. R. 1994, Solar Physics, 150, 347.  http://link.springer.com/10.1007/BF00712896
  * Bame et al. (1977) Bame, S. J., Asbridge, J. R., Feldman, W. C., & Gosling, J. T. 1977, Journal of Geophysical Research, 82, 1487.  http://doi.wiley.com/10.1029/JA082i010p01487
  * Benz (2008) Benz, A. O. 2008, Living Rev. Solar Phys, 5, 1.  http://www.livingreviews.org/lrsp-2008-1%5Cnhttp://www.astro.phys.ethz.ch/staff/benz/benz%5Cnhttp://creativecommons.org/licenses/by-nc-nd/2.0/de/
  * Borrini et al. (1981) Borrini, G., Gosling, J. T., Bame, S. J., Feldman, W. C., & Wilcox, J. M. 1981, Journal of Geophysical Research: Space Physics, 86, 4565.  http://doi.wiley.com/10.1029/JA086iA06p04565
  * Eselevich & Eselevich (2006) Eselevich, M. V., & Eselevich, V. G. 2006, Astronomy Reports, 50, 748
  * Feldman et al. (2005) Feldman, U., Landi, E., & Schwadron, N. A. 2005, Journal of Geophysical Research: Space Physics, 110, 1
  * Feldman et al. (1978) Feldman, W. C., Asbridge, J. R., Bame, S. J., & Gosling, J. T. 1978, Journal of Geophysical Research, 83, 2177
  * Fisk (2003) Fisk, L. A. 2003, Journal of Geophysical Research, 108, 1157.  http://doi.wiley.com/10.1029/2002JA009284
  * Fontenla et al. (2001) Fontenla, J. M., Avrett, E. H., & Loeser, R. 2001, 636.  http://arxiv.org/abs/astro-ph/0109416%0Ahttp://dx.doi.org/10.1086/340227
  * Fontenla et al. (1988) Fontenla, J. M., Reichmann, E. J., & Tandberg-Hanssen, E. 1988, The Astrophysical Journal, 329, 464.  http://adsabs.harvard.edu/doi/10.1086/166392
  * Fox et al. (2016) Fox, N. J., Velli, M. C., Bale, S. D., et al. 2016, Space Science Reviews, 204, 7.  http://link.springer.com/10.1007/s11214-015-0211-6
  * Goelzer et al. (2013) Goelzer, M. L., Smith, C. W., Schwadron, N. A., & McCracken, K. G. 2013, Journal of Geophysical Research: Space Physics, 118, 7525
  * Hirshberg (1973) Hirshberg, J. 1973, Reviews of Geophysics, 11, 115.  http://doi.wiley.com/10.1029/RG011i001p00115
  * Hunter (2007) Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90.  http://ieeexplore.ieee.org/document/4160265/http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4160265
  * Kasper et al. (2008) Kasper, J. C., Lazarus, A. J., & Gary, S. P. 2008, Physical Review Letters, 101, 261103.  http://link.aps.org/doi/10.1103/PhysRevLett.101.261103
  * Kasper et al. (2006) Kasper, J. C., Lazarus, A. J., Steinberg, J. T., Ogilvie, K. W., & Szabo, A. 2006, Journal of Geophysical Research, 111, A03105.  http://doi.wiley.com/10.1029/2005JA011442
  * Kasper et al. (2012) Kasper, J. C., Stevens, M. L., Korreck, K. E., et al. 2012, The Astrophysical Journal, 745, 162.  http://stacks.iop.org/0004-637X/745/i=2/a=162?key=crossref.3243440b8824f281eaf92619ea6e90c4
  * Kasper et al. (2007) Kasper, J. C., Stevens, M. L., Lazarus, A. J., Steinberg, J. T., & Ogilvie, K. W. 2007, The Astrophysical Journal, 660, 901.  http://stacks.iop.org/0004-637X/660/i=1/a=901
  * Kasper et al. (2016) Kasper, J. C., Abiad, R., Austin, G., et al. 2016, Space Science Reviews, 204, 131.  http://link.springer.com/10.1007/s11214-015-0206-3
  * Kasper et al. (2017) Kasper, J. C., Klein, K. G., Weber, T., et al. 2017, The Astrophysical Journal, 849, 126.  http://stacks.iop.org/0004-637X/849/i=2/a=126?key=crossref.a4fda357a12d19fd2ad1aa8a3897c78f
  * Kluyver et al. (2016) Kluyver, T., Ragan-kelley, B., Pérez, F., et al. 2016, Positioning and Power in Academic Publishing: Players, Agents and Agendas, 87
  * Laming (2004) Laming, J. M. 2004, The Astrophysical Journal, 614, 1063.  http://adsabs.harvard.edu/abs/2004ApJ...614.1063L
  * Laming (2015) —. 2015, Living Reviews in Solar Physics, 12, doi:10.1007/lrsp-2015-2
  * Laming & Feldman (2003) Laming, J. M., & Feldman, U. 2003, The Astrophysical Journal, 591, 1257
  * Maruca & Kasper (2013) Maruca, B. A., & Kasper, J. C. 2013, Advances in Space Research, 52, 723.  http://dx.doi.org/10.1016/j.asr.2013.04.006
  * Mauas et al. (2005) Mauas, P. J. D., Andretta, V., Falchi, A., et al. 2005, The Astrophysical Journal, 619, 604
  * Mckinney (2010) Mckinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 51 – 56
  * McMullin et al. (2016) McMullin, J. P., Rimmele, T. R., Warner, M., et al. 2016, 99061B.  http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2235227
  * Meyer (1991) Meyer, J. P. 1991, Advances in Space Research, 11, 269
  * Meyer (1993) —. 1993, Advances in Space Research, 13, 377
  * Millman & Aivazis (2011) Millman, K. J., & Aivazis, M. 2011, Computing in Science & Engineering, 13, 9\.  http://ieeexplore.ieee.org/document/5725235/
  * Müller et al. (2013) Müller, D., Marsden, R. G., St. Cyr, O. C., & Gilbert, H. R. 2013, Solar Physics, 285, 25
  * Ogilvie et al. (1995) Ogilvie, K. W., Chornay, D. J., Fritzenreiter, R. J., et al. 1995, Space Science Reviews, 71, 55.  http://link.springer.com/10.1007/BF00751326
  * Oliphant (2007) Oliphant, T. E. 2007, Computing in Science & Engineering, 9, 10.  http://ieeexplore.ieee.org/document/4160250/
  * Özgüç & Ataç (2001) Özgüç, A., & Ataç, T. 2001, in IAU Symposium, Vol. 203, Recent Insights into the Physics of the Sun and Heliosphere: Highlights from SOHO and Other Space Missions, ed. P. Brekke, B. Fleck, & J. B. Gurman, 125\.  http://adsabs.harvard.edu/abs/2001IAUS..203..125O
  * Perez & Granger (2007) Perez, F., & Granger, B. E. 2007, Computing in Science & Engineering, 9, 21\.  http://ieeexplore.ieee.org/document/4160251/
  * Rakowski & Laming (2012) Rakowski, C. E., & Laming, J. M. 2012, The Astrophysical Journal, 754, 65
  * Ramesh & Vasantharaju (2014) Ramesh, K. B., & Vasantharaju, N. 2014, Astrophysics and Space Science, 350, 479
  * Schwadron et al. (1999) Schwadron, N. A., Fisk, L. A., & Zurbuchen, T. H. 1999, The Astrophysical Journal, 521, 859
  * Schwenn (2006) Schwenn, R. 2006, Space Science Reviews, 124, 51
  * SILSO World Data Center (2018) SILSO World Data Center. 2018
  * Stakhiv et al. (2016) Stakhiv, M., Lepri, S. T., Landi, E., Tracy, P., & Zurbuchen, T. H. 2016, The Astrophysical Journal, 829, 117.  http://stacks.iop.org/0004-637X/829/i=2/a=117?key=crossref.b0b55b70491e273f167cae33b403f34c
  * Temmer et al. (2003) Temmer, M., Veronig, A., & Hanslmeier, A. 2003, Solar Physics, 215, 111
  * van der Walt et al. (2011) van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science & Engineering, 13, 22.  http://ieeexplore.ieee.org/document/5725236/
  * van Driel-Gesztelyi & Green (2015) van Driel-Gesztelyi, L., & Green, L. M. 2015, Living Reviews in Solar Physics, 12, 1.  http://link.springer.com/10.1007/lrsp-2015-1
  * Vanlommel et al. (2005) Vanlommel, P., Cugnon, P., Van Der Linden, R. A. M., Berghmans, D., & Clette, F. 2005, Solar Physics, 224, 113
  * Vauclair & Charbonnel (1991) Vauclair, S., & Charbonnel, C. 1991, in Challenges to Theories of the Structure of Moderate-Mass Stars (Berlin, Heidelberg: Springer Berlin Heidelberg), 37–41.  http://link.springer.com/10.1007/3-540-54420-8_48


# Diving Deep into Modes of Fact Hallucinations in Dialogue Systems

Souvik Das Sougata Saha Rohini K. Srihari
{souvikda, sougatas<EMAIL_ADDRESS>
Department of Computer Science and Engineering, University at Buffalo, NY.

###### Abstract

Knowledge Graph(KG) grounded conversations often use large pre-trained models
and usually suffer from fact hallucination. Frequently entities with no
references in knowledge sources and conversation history are introduced into
responses, thus hindering the flow of the conversation—existing work attempt
to overcome this issue by tweaking the training procedure or using a multi-
step refining method. However, minimal effort is put into constructing an
entity-level hallucination detection system, which would provide fine-grained
signals that control fallacious content while generating responses. As a first
step to address this issue, we dive deep to identify various modes of
hallucination in KG-grounded chatbots through human feedback analysis.
Secondly, we propose a series of perturbation strategies to create a synthetic
dataset named FADE (FActual Dialogue Hallucination DEtection
Dataset)111https://github.com/souvikdgp16/FADE. Finally, we conduct
comprehensive data analyses and create multiple baseline models for
hallucination detection to compare against human-verified data and already
established benchmarks.

##  1 Introduction

Knowledge-grounded conversational models often use large pre-trained models
Radford et al. (2019); Brown et al. (2020). These models are notorious for
producing responses that do not comply with the provided knowledge; this
phenomenon is known as hallucination Dziri et al. (2022b); Rashkin et al.
(2021b). Faithfulness to the supplementary knowledge is one of the prime
designing factors in these knowledge-grounded chatbots. If a response is
unfaithful to some given knowledge, it becomes uninformative and risks
jeopardizing the flow of the conversation. Despite retaining strong
linguistics abilities, these large language models(LM) inadequately comprehend
and present facts during conversations. LMs are trained to emulate
distributional properties of data that intensify its hallucinatory attributes
during test time.

Figure 1: Hallucination manifested by generated responses using GPT2Radford et
al. (2019) trained on KG triples can be more nuanced.

On the one hand, many prior works Wiseman et al. (2017); Parikh et al. (2020);
Tuan et al. (2019) have suggested training these models on external data to
ensure faithfulness may lead to a source-reference divergence problem, where
the reference contains additional factual information. To address this problem
holistically, Dziri et al. has proposed a two-step generate-then-refine
approach by augmenting conventional dialogue generation with a different
refinement stage enabling the dialogue system to correct potential
hallucinations by querying the KG. Also, this work employs a token-level
hallucination classifier trained on a synthetic dataset constructed using two
perturbation strategies 222(1) Extrinsic perturbation: Dziri et al. have
swapped an entity with a different entity of the same type and not present in
$1$-hop subgraph. (2) Intrinsic perturbation: they have swapped an entity with
its object or vice versa, taken from the golden $1$-hop subgraph.. Though this
method has clear benefits, the hallucination perturbation strategies proposed
in this work might fail to capture some of the subtle attributions of a
factual generative model. As illustrated in Figure 1, neural models can inject
hallucinated entities into responses that are present in the $k$-hop KG and
are deceptively similar to what is expected. Also, if we cannot detect these
elusive hallucinations beforehand, it will cause a cascading effect and
amplify hallucinations in subsequent turns See and Manning (2021).

On the other hand, relying on human annotations is challenging due to error-
prone collection protocols and human ignorance to complete the tasks with care
Smith et al. (2022). Prior research Dziri et al. (2022c) shows that knowledge-
grounded conversational benchmarks contain hallucinations promoted by a design
framework that encourages informativeness over faithfulness. As studied by
Dziri et al., when the annotators are asked to identify hallucination in a
response, there is a high chance of error due to lack of incentive, personal
bias, or poor attention to the provided knowledge.

See and Manning have studied different shortcomings in a real-time neural
model. In this work, based on some of the findings of See and Manning, like
repetitive and unclear utterances promoting hallucination, we extend the
already defined modes of hallucinations Maynez et al. (2020); Dziri et al.
(2021a). Our contributions to this work are threefold:

  * •

We extend fact hallucination in KG-grounded dialogue systems into eight
categories. To understand the degree to which our defined classes exist in
real-life data, we conduct a systematic human evaluation of data generated by
a state-of-the-art neural generator.

  * •

Since human annotation is expensive and often inaccurate, we design a series
of novel perturbation strategies to simulate the defined ways of fact
hallucinations and build a set of synthetic datasets collectively named as
FADE (FActual Dialogue Hallucination DEtection Dataset).

  * •

We create multiple pre-trained model-based baselines and compare the
performances on several constituent and mixed datasets. To assess our
dataset’s generalization capability, we perform zero-shot inference on BEGIN
Dziri et al. (2021b), and FaithDial Dziri et al. (2022a) datasets, which
encompasses all categories of hallucinated responses.

##  2 Different Modes of Hallucination in KG-grounded Dialogue Systems

###  2.1 Background

We focus on the task of detecting hallucinated spans in dialogues that are
factually grounded on factoids derived from multi-relational graphs
$\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{R})$, termed as Knowledge-
Graphs(KG). Each KG consists of an directed edge triples
$t=\left<\texttt{[SBJ], [PRE], [OBJ]}\right>$, where $\texttt{[SBJ],
[OBJ]}\in\mathcal{V}$ are nodes denoting subject and object entities and
$\texttt{[PRE]}\in\mathcal{R}$ is a predicate which can be understood as a
relation type. Primarily, a neural dialogue system is guilty of generating
hallucinated text when a valid path in the $k$-hop sub-graph
$\mathcal{G}^{k}_{c}\in\mathcal{G}$ of the original KG anchored around a
context entity $c$ does not support it.

Our study extends the work of Dziri et al. (2021a) where they specifically
explore two broad circumstances – extrinsic and intrinsic to the provided KG,
under which LMs are likely to exhibit unfaithful behavior. Though this
categorization is beneficial for detecting hallucinations, these categories
can be further subdivided into subcategories, which are described in §2.3.

###  2.2 Base Dataset

We use OpenDialKG Moon et al. (2019), a crowded-sourced English dialogue
dataset where two workers are paired to chat about a particular topic(mainly
movie, music, sport, and book). We use this dataset for training a GPT2-based
model for generating data for human feedback analysis and creating the
perturbed datasets. More details about the dataset can be found in §C

###  2.3 Definitions

Figure 2: Illustration of our defined categories of fact hallucinations in KG-
grounded dialogue systems

We define below several categories of fact hallucination, comprehensive
illustrations of each types are provided in Figure 2. In addition we have
included detailed descriptions of each definitions in §A

  1. (a)

(Extrinsic-Soft). An extrinsic-soft hallucination corresponds to an utterance
that brings a new span of text which is similar to the expected span but does
not correspond to a valid triple in $\mathcal{G}^{k}_{c}$.

  2. (b)

(Extrinsic-Hard). An extrinsic-hard hallucination corresponds to an utterance
that brings a new span of text which is different from the expected span and
does not correspond to a valid triple in $\mathcal{G}^{k}_{c}$.

  3. (c)

(Extrinsic-Grouped). An extrinsic-grouped hallucination corresponds to an
utterance that brings a new span of text which is different from the expected
span but is of a specific predefined type and does not correspond to a valid
triple in $\mathcal{G}^{k}_{c}$.

  4. (d)

(Intrinsic-Soft). An intrinsic-soft hallucination corresponds to an utterance
that misuses any triple in $\mathcal{G}^{k}_{c}$ such that there is no direct
path between the entities but they are similar to each other.

  5. (e)

(Intrinsic-Hard). An intrinsic-hard hallucination corresponds to an utterance
that misuses any triple in $\mathcal{G}^{k}_{c}$ such that there is no direct
path between the entities and they are not related in any form.

  6. (f)

(Intrinsic-Repetitive). An intrinsic-repetitive hallucination corresponds to
an utterance that either misuses [SBJ] or [OBJ] in $\mathcal{G}^{k}_{c}$ such
that there is no direct path between the entities but the entity has
previously occurred in conversational history..

  7. (g)

(History Corrupted- Intrinsic/ Extrinsic). A history
corrupted(intrinsic/extrinsic) hallucination corresponds to an utterance that
is subjected to intrinsic or extrinsic hallucination which is influenced by
hallucinated entities in conversational history.

###  2.4 Human Feedback Analysis

To study the extent to which the previously described modes of hallucination
exist in a real-world system, we did human feedback analysis on responses
generated using a GPT2-based generative model fine-tuned on OpenDialKG as
described by Dziri et al.. We sampled 200 responses each from four different
decoding strategies, Greedy, Beam Search, and Nucleus Sampling, with a
probability of 0.9 and 0.5. For each dialogue instance, we crowd-source human
judgment by soliciting evaluations from 2 different annotators(with a high
approval rating) from Amazon Mechanical Turk(AMT)(Details in §B). One computer
science graduate student additionally verified the Human Intelligence Task
(HITS). For examples where hallucination was present, we asked the workers to
identify the type of hallucination(examples of different types of
hallucinations were shown in the instruction). The result of the human
feedback is exhibited in Table 1. We rejected $21\%$ of the HITS because of
poor quality; we reported the average Krippendorf alpha coefficient to be 0.74
on the remaining annotations, indicating a moderate to a high agreement. Using
Table 1 we made these observations:

  * •

Extrinsic-soft hallucination is the dominant form of hallucination. Also, this
bolsters our prior observation that LMs generate entities similar to the
golden entity.

  * •

Comparatively less amount of hallucinations was seen in responses generated
using beam search decoding scheme, though the percentage of extrinsic-hard
hallucination was higher than greedy decoding.

  * •

Intrinsic-hard hallucination appears to be the least among all types. This
suggests LM will always try to learn something from the given KG triples;
generating something dissimilar will have a very low probability.

GPT2-KG | Greedy | Beam Search | Nucleus 0.9 | Nucleus 0.5
---|---|---|---|---
Extrinsic-Soft | 10.91 | 8.8 | 15.5 | 14.77
Extrinsic-Hard | 3.45 | 4.22 | 8.3 | 9.8
Extrinsic-Grouped | 1.12 | 1 | 0.44 | 1.6
History Corrupted-Extrinsic | 3.3 | 3.1 | 2.33 | 1.1
Intrinsic-Soft | 1.2 | 1.38 | 0.8 | 0.3
Intrinsic-Hard | 0.2 | 0.8 | 1.1 | 2
Intrinsic-Repetitive | 0.2 | 0.8 | 1.8 | 4
History Corrupted-Intrinsic | 0.7 | 0.5 | 1.33 | 3.3
Extrinsic Total | 18.78 | 17.12 | 26.57 | 27.27
Intrinsic Total | 2.3 | 3.48 | 5.03 | 9.6
Total | 21.08 | 20.6 | 31.6 | 36.87

Table 1: Fine-grain human feedback analysis

##  3 Dataset Creation

FADE is a collection of datasets consisting of component datasets created
using several perturbations and a set of mixed datasets constructed using the
component datasets.

###  3.1 Perturbation Strategies

Extrinsic Hallucination All the entities present in OpenDialKG undergo a
indexing process. At first, using Spacy we determine the named entity type
333https://spacy.io/api/entityrecognizer for each entity, and create BM25
indexes444https://solr.apache.org/ for each entity type. Each KG triple
corresponding to an entity is represented in this format – "[SBJ] [PRE] [OBJ]"
and denoted as $t_{i}$. Now, for an entity($e_{i}$) we create a document
$d_{i}=\mathrm{concat}(t_{1},t_{2},..t_{n})$, $n$ is the number of KG-triples
for that entity. After this, we index $d_{i}$ and $e_{i}$ in the index
corresponding to the entity type. During the perturbation process, we retrieve
all the KG-triples for the entity we want to perturb and form 3 queries for
each triple by permuting ([SBJ],[PRE],[OBJ]). Then based on the type of
extrinsic hallucination, we query the indices to get the document scores in
the following way:
$\mathrm{scores}=\mathrm{average}(\\{BM25(q_{i},d_{j})\\}_{i\in{(s,r,o)},j\in{(0,n)}})$,
the selection criteria of the perturbed entities are provided in table 2.

The groups for extrinsic-grouped hallucination are mentioned in Table 10.
During the selection process, we iteratively check whether the perturbed
entity exists in the conversation history, matches with the actual entity, and
has appeared in the 1-hop sub-graph of the original entity. If an occurrence
is found, we proceed to the following best entity.

Hallucination Type | Index Type | Selection Criteria
---|---|---
Soft | Same as original entity |  $e_{i}$ with max document score
Hard | Same as original entity |  $e_{i}$ with min document score
Grouped | Same as one predefined type, selected randomly | same as soft

Table 2: Extrinsic hallucination perturbed entity selection criteria

Intrinsic Hallucination Here, we dynamically create a BM25 index and index all
the KG triples in the 1-hop sub-graph of the original entity. Again, a KG
triple is represented in the same fashion as in extrinsic hallucination –
"[SBJ] [PRE] [OBJ]". The goal here is to select entities that are similar or
dissimilar to the original entities and present in the 1-hop graph. To achieve
that, we follow a hybrid triple retrieval approach to score each triple
associated with the original entity. First, we use the final hidden layer of a
pre-trained GPT2 to obtain initial embeddings for each node in
$\mathcal{G}^{k}_{c}$ (for details, check §D.3). A query is formed by using
Equation 1 each triple in $\mathcal{G}^{k}_{c}$ is scored using a similarity
scoring system as described in Equation 3.

$\mathbf{q}=\sum_{i\in\\{s,r,o\\}}\frac{\varepsilon}{p(q_{i})+\varepsilon}\
\mathbf{v_{q_{i}}}$ (1)

Here $\varepsilon$ is a free term parameter (§D.2), $p(q_{i})$ is unigram
probability of the query term and $\mathbf{v_{q_{i}}}$ is the embedding for
each query term(here query terms are [SBJ], [PRE] ,[OBJ] of the original
entity).

$\mathbf{n_{i}}=\frac{\varepsilon}{p(s)+\varepsilon}\
\mathbf{v_{s}}+\frac{\varepsilon}{q(r)+\varepsilon}\
\mathbf{v_{r}}+\frac{\varepsilon}{p(o)+\varepsilon}\ \mathbf{v_{o}}$ (2)

$\mathbf{n_{i}}$ in Equation 2 represents a triple embedding in
$\mathcal{G}^{k}_{c}$, when $q(r)$ represents the rarity of the relationship
term in the subgraph, high occurrence is penalized, rest terms are analogous
to Equation 1.

$\mathrm{EntitySimilarity(Q,t)}=cos(\mathbf{q},\mathbf{n_{i}})$ (3)

Now, we query the BM25 index that we have created before with a simple query
using the original triple: "[SBJ] [PRE] [OBJ]" and get the score for each of
the triple($t$). Finally, we get the final scores using Equation 4.

$\begin{split}\mathrm{Score(Q,t)}=\beta\mathrm{EntitySimilarity(Q,t)}\\\
+(1-\beta)\mathrm{BM25(Q,t)}\end{split}$ (4)

Here $0<\beta<1$.

Hallucination Type | Selection Criteria
---|---
Soft |  [SUB] or [OBJ] with max triple score
Hard |  [SUB] or [OBJ] with min triple score
Repetitive | same as soft, should be occurring in the conversation history

Table 3: Intrinsic hallucination perturbed entity selection criteria

We select the perturbed entities based on the scores and selection criteria as
defined in Table 3. Like extrinsic hallucinations, we iteratively filter the
best-scored entity until it does not match the original entity or appears in
history.

History Corrupted Hallucination Conversational history is corrupted using
intrinsic or extrinsic corruption strategy. We select the last $k$ turns of
the conversation and randomly perturb the entities. We also ensure that at
least $50\%$ of the previous $k$ turns are corrupted.

###  3.2 Dataset Analysis

Below we provide data statistics and characterize the composition and
properties of the datasets that are generated using our proposed perturbation
strategies.

####  3.2.1 Data Statistics

Type | Perturbed | Non-perturbed |  | Turn with
---
perturbation>2
soft | 12752 | 64634 | 558
hard | 8540 | 68872 | 8254
grouped | 22858 | 54542 | 11296
history-corrupt | 8534 | 68878 | 8247

Table 4: Extrinsic hallucination data statistics

Type | Perturbed | Non-perturbed |  | Turn with
---
perturbation>2
soft | 18560 | 58558 | 5
hard | 18605 | 58534 | 6
repetitive | 9712 | 67560 | 0
history-corrupt | 18597 | 58542 | 6

Table 5: Intrinsic hallucination data statistics

Table 5 and 5 shows the statistics of datasets created using different
perturbation strategies. The base dataset contains 77,430 data points.
However, the perturbed turns in each of these datasets are quite low in
comparison. This low number is because not every entity in an utterance has a
valid KG path. For extrinsic hallucination, $\sim$12,000 to $\sim$23,000
utterances were perturbed, and $\sim$550 to $\sim$11,300 utterances have
multiple perturbations. The number of perturbed data points for intrinsic
hallucination is less than extrinsic($\sim$9,000 to $\sim$18,000). The number
of utterances with multiple perturbations is negligible due to the many checks
the perturbed entities go through(for example, whether the KG path is present,
has already occurred or not, etc.) To train and evaluate models, we vary the
size of the train split in this range of $10\%$ to $30\%$555sequential split
with a step of $2.5\%$, keeping in mind to avoid overfitting. The remaining
data is split into equal halves for validation and testing.

####  3.2.2 Parsing Features

In Figure 3 we show the top 10 Named Entity Recognition(NER) tags as
identified by the Spacy library in extrinsic hallucinations. For extrinsic-
soft hallucination, most NER tags are of type PERSON. This corresponds to the
fact that the original entities in the base dataset are primarily related to
movies, books, and music. In extrinsic-soft hallucination, the associated
PERSON name is changed to a closely affiliated person, or a movie name is
changed to its director’s name. In contrast, the distribution of NER tags is
uniform for extrinsic-hard hallucination.

Figure 3: NER distribution in Extrinsic-soft and hard hallucination

Figure 4 and 5 shows the top-10 relations of the perturbed entity with the
original entity in both intrinsic-soft and hard hallucinations and the
corresponding value in their counterparts. In intrinsic-soft hallucination,
more relevant relations are selected like "release year", "starred actors",
"written by", etc. On the other hand, in intrinsic hard hallucination, more
unusual relations like "Country of Origin", and "Country of Nationality" were
among the top relations.

Figure 4: Top 10 relation in perturbed KG triples in intrinsic-soft
hallucination Figure 5: Top 10 relation in perturbed KG triples in intrinsic-
hard hallucination

###  3.3 Mixing Datasets

Since in actual data, all kinds of hallucinations are expected to occur. We
mix the previously constructed datasets in specific proportions to create a
more challenging dataset. Table 11 shows the different mixing ratios for four
types of datasets is as follows: Observed: We try to mimic the observed data,
which is shown in §2.4, we take an average of percentages in for all the
decoding strategies. Balanced: Goal here is to create a balanced dataset
between hallucinated and non-hallucinated turns, each type of hallucination is
also balanced. Extinsic+: In this scenario, we increase the percentages of
extrinsic-soft, hard, and grouped by a factor of 2, 1.5, and 1.5,
respectively. Intrinsic+: here we increase the percentages of intrinsic-soft,
hard and repetitive by a factor of 1.5. More details in §D.4.

###  3.4 Human Verification

To verify whether our proposed perturbation strategies inject hallucinations
in the original data, we randomly sample 150 examples from each of the mixed
dataset’s test splits. Subsequently, these samples were randomly ordered to
form a consolidated sample of 600 data points annotated by at least three AMT
workers, with the same setting as described in §2.4. Additionally, the
graduate student verified where the hallucinations adhere to the perturbation
norms. Krippendorff’s alpha were 0.88 and 0.76 among workers, and workers with
perturbed data(average), indicating a very high agreement. Since our
perturbation strategies are purely deterministic, we kept a large-scale human
verification of the automatically annotated data outside the scope of this
work. We create a human-verified dataset of 500 samples, 300 taken from this
set and 200 from the human feedback study 2.4.

##  4 Task

To identify utterances that contain hallucinations and to locate the entities
of concern. We create two tasks:

  1. 1.

Utterance classification: Given the dialog history $\mathcal{D}$, knowledge
triples $\mathcal{K}_{n}$ and the current utterance $\overline{x}_{n+1}$ we
classify $\overline{x}_{n+1}$ is hallucinated or not.

  2. 2.

Token classification: Given $\mathcal{D}$, $\mathcal{K}_{n}$ and
$\overline{x}_{n+1}$, we need to perform sequence labelling on
$\overline{x}_{n+1}$ and identify the hallucinated spans.

##  5 Baseline Models

As an initial effort toward tackling the suggested hallucination detection
task, we create several baseline detection models based on pre-trained
transformer models, including BERT, XLNet, and RoBERTa. These transformer-
based models represent the state-of-the-art and can potentially better
leverage context or embedded world knowledge to detect self-contradictory or
anti-commonsense content.

For training the utterance classifier, given $\mathcal{D}$, $\mathcal{K}_{n}$
and $\overline{x}_{n+1}$, we fine tune a pre-trained model $\mathcal{M}$ to
predict binary hallucinated label $\mathbf{y}$ for $\overline{x}_{n+1}$ .
Here, $\mathcal{D}$ and $\mathcal{K}_{n}$ are considered as sequence A with
token type ids as $0$ and $\overline{x}_{n+1}$ is considered as sequence B
with token type ids as $1$. During inference, from the last hidden states
$\mathbf{H}\in\mathbb{R}^{l\times h}$ ($h$, $l$ are hidden size and sequence
length, respectively), then we obtain the representation
$\mathbf{w}\in\mathbb{R}^{h}$ by max pooling(i.e.,
$\mathbf{w}=max\\_pool(\mathbf{H})$). We then pass $\mathbf{w}$ through a MLP
layer with a $tanh$ activation to get the binary label $y\in\\{0,1\\}$. During
training time, we fine-tune the model using cross entropy objective between
the predicted labels and the actual labels.

Similarly, for training the sequence classifier, we fine-tune a pre-trained
model $\mathcal{M}_{s}$. At first, we encode $\mathcal{D}$, $\mathcal{K}_{n}$
and $\overline{x}_{n+1}$ using $\mathcal{M}_{s}$ to get the last hidden states
$\mathbf{H}\in\mathbb{R}^{l\times h}$, ($h$, $l$ are hidden size and sequence
length, respectively). Instead of doing a binary classification of each token,
we adopt a $BILOU$ encoding scheme. The hidden states are passed through an
MLP layer with a $tanh$ activation to get the 5-way label
$y\in\\{B,I,L,O,U\\}$. During training time, we fine-tune the model using a
cross-entropy objective between the predicted and actual labels.

##  6 Experimental Setup

Dataset | Best Model | Token Level | Utterance Level
---|---|---|---
F1 | P | R | F1 | P | R |  G-Mean($\uparrow$) |  BSS($\downarrow$) | AUC
extrinsic-grouped | BERT(base-uncased) | 80.69 | 80.56 | 80.82 | 91.30 | 91.80 | 90.81 | 93.58 | 5.29 | 93.62
extrinsic-hard | XLNet(base-cased) | 72.12 | 71.98 | 72.25 | 87.36 | 87.13 | 87.60 | 92.80 | 2.93 | 92.96
extrinsic-history-corrupt | XLNet(base-cased) | 72.38 | 72.35 | 72.40 | 88.10 | 87.86 | 88.34 | 93.24 | 2.75 | 93.38
extrinsic-soft | BERT(base-uncased) | 64.09 | 69.22 | 59.67 | 74.80 | 81.96 | 68.80 | 81.62 | 8.03 | 82.81
intrinsic-hard | XLNet(base-cased) | 84.44 | 85.08 | 83.81 | 90.88 | 92.88 | 88.97 | 93.24 | 4.48 | 93.34
intrinsic-history-corrupt | XLNet(base-cased) | 83.67 | 82.27 | 85.11 | 91.30 | 91.86 | 90.74 | 93.97 | 4.34 | 94.02
intrinsic-repetitive | RoBERTa(base) | 82.70 | 82.76 | 82.64 | 88.01 | 89.51 | 86.55 | 92.31 | 3.15 | 92.50
intrinsic-soft | RoBERTa(base) | 78.80 | 80.19 | 77.45 | 87.10 | 90.54 | 83.92 | 90.26 | 6.22 | 90.50

Table 6: Test benchmark (numbers in percentages (%)) for component datasets,
models trained on 25% of the total dataset.

Dataset | Best Model | Token Level | Utterance Level
---|---|---|---
F1 | P | R | F1 | P | R |  G-Mean($\uparrow$) |  BSS($\downarrow$) | AUC
balanced | RoBERTa-base | 73.41 | 68.75 | 78.74 | 88.24 | 83.85 | 93.12 | 86.21 | 13.14 | 86.47
observed | XLNet(base-cased) | 63.44 | 57.98 | 70.03 | 77.71 | 71.05 | 85.73 | 85.40 | 14.73 | 85.40
intrinsic+ | RoBERTa-base | 75.05 | 71.11 | 79.44 | 90.16 | 86.52 | 94.12 | 84.51 | 12.78 | 85.00
extrinsic+ | XLNet(base-cased) | 75.59 | 70.79 | 81.10 | 90.75 | 86.77 | 95.11 | 83.21 | 12.65 | 83.95

Table 7: Test benchmark (numbers in percentages (%)) for mixed datasets,
models trained on 25% of the total dataset.

Baseline configurations we experiment with a variety of pre-trained models via
Hugging Face Transformers, including BERT-base-uncased(110M), RoBERTa-
base(125M) and XLNet-base-cased(110M). Though using large or medium versions
of these models will produce better results, we refrain from using those
models as scaling large models in production is costly. More details about
training parameters can be found in §E

We also experimented with model architecture as follows: (i) Varied the length
of the history (ii) Experimented with max/ mean pooling. (iii) Whether to
concatenate the hidden states corresponding to $\mathcal{K}_{n}$ with the
hidden states corresponding to $\overline{x}_{n+1}$ before passing them
through the MLP layer. (iv) Using a CRF layer instead of MLP for predicting
labels in the sequence tagger. The best configuration uses 4 turns of
conversational history, max pooling, it does not concatenate hidden states of
$\mathcal{K}_{n}$ with hidden states of $\overline{x}_{n+1}$ and uses a
2-layer MLP.

Evaluation metrics We evaluate the baselines with formal classification
metrics, including precision, recall, and F1 for the hallucination sequence
tagger. For the utterance-level hallucination classifier, we report accuracy,
precision, recall, F1, and AUC (Area Under Curve) for ROC. We also use the
G-Mean metric Espíndola and Ebecken (2005), which measures the geographic mean
of sensitivity and specificity. We also employ the Brier Skill Score (BSS)
metric Center (2005), which computes the mean squared error between the
reference distribution and the hypothesis probabilities.

##  7 Results and Discussion

Figure 6: Positive and negative model predictions

Baseline performance Table 6 and Table 7 show the baseline performance for the
component datasets and mixed datasets. In both the settings, the utterance
level hallucination classifier performs better than the token tagger in terms
of F1. It can be inferred from Table 6 that, on average, it is comparatively
easier to detect intrinsic hallucinations than extrinsic hallucinations; due
to grounding on external knowledge, which indicates the validity of our
perturbation techniques. However, comparing the occurrence statistics from
Table 1, it is noticed that extrinsic-soft hallucination, which has the least
F1 score among all types, has the highest occurrences. In extrinsic-grouped
and extrinsic-soft hallucinations, it is interesting that BERT performs better
than the other pre-trained models. Now for mixed datasets, we ran inference on
the test set of observed dataset, as expected F1 scores(for utterance
classifier and token level tagger) of the observed dataset are low as compared
to other datasets due to high percentage of extrinsic-soft hallucination.
Among other mixed datasets, the XLNet model fine-tuned on extrinsic+ dataset
performs best in terms of F1 scores.

Figure 7: Generalisation capability of RoBERTa-large model fine-tuned using
multiple splits of intrinsic-history-corrupt dataset

Performance on human-verified data We test the best performing models fine-
tuned on our mixed datasets on human-verified data as described in §3.4. Using
the existing benchmark and baseline models, we also perform a zero-shot
inference on the human-verified data. From Table 8, it is clear that the
models fine-tuned on existing benchmark data cannot understand fact
hallucination, especially when entities are misplaced. On the other hand,
models trained on our datasets have F1 scores over 90% and outperform the
current baseline by 10.16% and 17.5% in the two tasks using a pre-trained
model with fewer parameters. This suggests that identifying abrupt fact
hallucination is more challenging than other types of hallucination(like
presenting more data than expected), which are more commonly exhibited in the
benchmark datasets.

Fine-tuned on | Pretrain Model |  | F1
---
(Utterance-level)
| F1
---
(Token-level)
MNLI | RoBERTa-large | 12.5 | -
BEGIN | RoBERTa-large | 15.4 | -
FaithDial | RoBERTa-large | 22.1 | -
Intrin-ExtrinDziri et al. (2021a) | RoBERTa-large | 83.81 | 68.2
balanced | RoBERTa-base | 92.27 | 78.61
observed | XLNet(base-cased) | 90.15 | 70.27
extrinsic+ | XLNet(base-cased) | 93.97* | 85.7*
intrinsic+ | RoBERTa-base | 93.01 | 84.33

Table 8: Performance of several benchmark models and models trained on FADE on
the 500 human-verified data( *p-value < 0.001))

Fine-tuned on | Model | BEGIN | FaithDial
---|---|---|---
MNLI(3-way)Dziri et al. (2021b) | T5 | 49.5 | -
MNLIDziri et al. (2022a) | RoBERTa-large | 61.1 | 81.6
intrinsic_hard | RoBERTa-base | 37.12 | 51.34
intrinsic_history_corrupt | RoBERTa-base | 43.23 | 63.11
intrinsic_hard | RoBERTa-large | 44.42 | 64.1
intrinsic_history_corrupt | RoBERTa-large | 55.11 | 71.43

Table 9: Zero-sort inference F1 scores on BEGIN and FaithDial benchmarks using
utterance classification models trained on FADE

Generalisability We make zero-shot inference on BEGIN and FaithDial datasets’
test splits. To make a fair comparison with the benchmark models, we further
fine-tune roberta-large model on our datasets. Table 9 shows that F1 scores
obtained from our best models underperform the best performing baseline by 6%
in BEGIN dataset and 10.17% in the FaithDial dataset. Even though the
performance is low, we have to understand that the benchmark datasets contain
hallucinations that are fundamentally very different from fact hallucinations.
Also, we notice that models trained on intrinsic hallucination perform the
best because the hallucinatory responses in the benchmark dataset do not
deviate much from the evidence. To estimate how much training data is optimum
for generalisability, we ran inference on benchmark datasets using models
fine-tuned to 10% to 30% (with a step of 2.5%) data in train split. As shown
in Figure 7 approximately 25% is found to be optimum.

Model Predictions We visualized the predictions on different datasets in
Figure 6. Our models were able to easily identify the hallucinated entities as
shown in Figure 6a here "The Departed" is a movie in which "Mark Wahlberg" has
acted but is not related to the movie discussed in the context, i.e., "The
Italian Job". Similarly, predictions made on the FaithDial dataset(Figure 6c)
show that our models could produce accurate predictions when the response is
generating something that is not expected, but the hallucination has
similarities with the evidence. Our model sometimes fails to understand when
the history is convoluted(Figure 6b)).

##  8 Related Work

Hallucination in Dialogue Systems Hallucination in knowledge-grounded dialogue
generation system is an emerging area of research Roller et al. (2021); Mielke
et al. (2020); Shuster et al. (2021); Rashkin et al. (2021b); Dziri et al.
(2021a). Prior work addressed this issue by conditioning generation on control
tokens Rashkin et al. (2021b), by training a token level hallucination critic
to identify troublesome entities and rectify them Dziri et al. (2021a) or by
augmenting a generative model with a knowledge retrieval mechanism Shuster et
al. (2021). Though beneficial, these models are trained on noisy training data
Dziri et al. (2022b) which can amplify the hallucinations further. Closest to
our work Dziri et al. (2021a) has created a hallucination critic using
extrinsic-intrinsic corruption strategies. In contrast, we create more fine-
grained corruption strategies so that hallucinated data mimics the
attributions of a neural chat module.

Hallucination Evaluation Recently several benchmarks have been introduced,
such as BEGINDziri et al. (2021b), DialFactGupta et al. (2022), FaithDialDziri
et al. (2022a) and Attributable to Identified Sources (AIS) Rashkin et al.
(2021a) framework. Though these methods can serve as a decent benchmarking
system, their performance in detecting entity-level hallucination is unknown.
In this work, we further contribute to this problem by proposing an entity-
level hallucination detector trained on data created by various fine-grained
perturbation strategies.

##  9 Conclusion

In this work, we have analyzed the modes of entity-level fact hallucination,
which is an open problem in KG-grounded dialogue systems. Through a human
feedback analysis, we demonstrate that these KG-grounded neural generators
manifest more nuanced hallucinations than straightforward studied approaches.
We have proposed fine-grained perturbation strategies to create a dataset that
mimics the real-world observations and create a series of datasets
collectively known as FADE. Our entity-level hallucination detection model can
predict hallucinated entities with an F1 score of 75.59% and classify whether
an utterance is hallucinated or not with an F1 score of 90.75%. Our models can
generalize well when zero-shot predictions are made on benchmarks like BEGIN
and FaithDial, indicating our perturbation strategies’ robustness. This work
can be extended by devising more sophisticated perturbation mechanisms, which
can simulate other types of hallucinations.

## Limitations

The major limitations of this work are as follows:

  * •

The token-level hallucination classifier and utterance-level hallucination
classifier can have contradictory results; however, this happens in a small
percentage of data.

  * •

Models trained on extrinsic datasets do not generalize well on the benchmark
datasets, as the benchmark dataset contains hallucination mostly related to
the evidence provided.

## Acknowledgements

We thank the anonymous reviewers for providing valuable feedback on our
manuscript. This work is partly supported by NSF grant number IIS-2214070. The
content in this paper is solely the responsibility of the authors and does not
necessarily represent the official views of the funding entity.

## References

  * Bast et al. (2014) Hannah Bast, Florian Bäurle, Björn Buchhold, and Elmar Haußmann. 2014\.  Easy access to the freebase dataset.  In _Proceedings of the 23rd International Conference on World Wide Web_ , WWW ’14 Companion, page 95–98, New York, NY, USA. Association for Computing Machinery.
  * Brown et al. (2020) Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020.  Language models are few-shot learners.
  * Center (2005) NOAA-CIRES Climate Diagnostics Center. 2005.  Brier skill scores, rocs, and economic value diagrams can report false skill.
  * Dziri et al. (2022a) Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti, and Siva Reddy. 2022a.  Faithdial: A faithful benchmark for information-seeking dialogue.
  * Dziri et al. (2021a) Nouha Dziri, Andrea Madotto, Osmar Zaïane, and Avishek Joey Bose. 2021a.  Neural path hunter: Reducing hallucination in dialogue systems via path grounding.  In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing_ , pages 2197–2214, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  * Dziri et al. (2022b) Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022b.  On the origin of hallucinations in conversational models: Is it the datasets or the models?
  * Dziri et al. (2022c) Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022c.  On the origin of hallucinations in conversational models: Is it the datasets or the models?
  * Dziri et al. (2021b) Nouha Dziri, Hannah Rashkin, Tal Linzen, and David Reitter. 2021b.  Evaluating groundedness in dialogue systems: The begin benchmark.
  * Espíndola and Ebecken (2005) Rogério P Espíndola and Nelson FF Ebecken. 2005.  On extending f-measure and g-mean metrics to multi-class problems.  _WIT Transactions on Information and Communication Technologies_ , 35.
  * Gupta et al. (2022) Prakhar Gupta, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022.  DialFact: A benchmark for fact-checking in dialogue.  In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 3785–3801, Dublin, Ireland. Association for Computational Linguistics.
  * Maynez et al. (2020) Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020.  On faithfulness and factuality in abstractive summarization.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 1906–1919, Online. Association for Computational Linguistics.
  * Mielke et al. (2020) Sabrina J. Mielke, Arthur Szlam, Y-Lan Boureau, and Emily Dinan. 2020.  Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness.
  * Moon et al. (2019) Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019.  OpenDialKG: Explainable conversational reasoning with attention-based walks over knowledge graphs.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 845–854, Florence, Italy. Association for Computational Linguistics.
  * Parikh et al. (2020) Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020.  ToTTo: A controlled table-to-text generation dataset.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 1173–1186, Online. Association for Computational Linguistics.
  * Radford et al. (2019) Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019.  Language models are unsupervised multitask learners.  _OpenAI blog_ , 1(8):9.
  * Rashkin et al. (2021a) Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter. 2021a.  Measuring attribution in natural language generation models.
  * Rashkin et al. (2021b) Hannah Rashkin, David Reitter, Gaurav Singh Tomar, and Dipanjan Das. 2021b.  Increasing faithfulness in knowledge-grounded dialogue with controllable features.  In _Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)_ , pages 704–718, Online. Association for Computational Linguistics.
  * Roller et al. (2021) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, and Jason Weston. 2021.  Recipes for building an open-domain chatbot.  In _Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume_ , pages 300–325, Online. Association for Computational Linguistics.
  * See and Manning (2021) Abigail See and Christopher Manning. 2021.  Understanding and predicting user dissatisfaction in a neural generative chatbot.  In _Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue_ , pages 1–12, Singapore and Online. Association for Computational Linguistics.
  * Shuster et al. (2021) Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021.  Retrieval augmentation reduces hallucination in conversation.  In _Findings of the Association for Computational Linguistics: EMNLP 2021_ , pages 3784–3803, Punta Cana, Dominican Republic. Association for Computational Linguistics.
  * Smith et al. (2022) Eric Smith, Orion Hsu, Rebecca Qian, Stephen Roller, Y-Lan Boureau, and Jason Weston. 2022.  Human evaluation of conversations is an open problem: comparing the sensitivity of various methods for evaluating dialogue agents.  In _Proceedings of the 4th Workshop on NLP for Conversational AI_ , pages 77–97, Dublin, Ireland. Association for Computational Linguistics.
  * Tuan et al. (2019) Yi-Lin Tuan, Yun-Nung Chen, and Hung-yi Lee. 2019.  DyKgChat: Benchmarking dialogue generation grounding on dynamic knowledge graphs.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 1855–1865, Hong Kong, China. Association for Computational Linguistics.
  * Wiseman et al. (2017) Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017.  Challenges in data-to-document generation.  In _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing_ , pages 2253–2263, Copenhagen, Denmark. Association for Computational Linguistics.
  * Wolf et al. (2020) Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020.  Transformers: State-of-the-art natural language processing.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations_ , pages 38–45, Online. Association for Computational Linguistics.

##  Appendix A Definition Details

  1. Figure 8: Extrinsic Hallucination
  2. (a)

(Extrinsic-Soft). An extrinsic-soft hallucination corresponds to an utterance
that brings a new span of text which is similar to the expected span but does
not correspond to a valid triple in $\mathcal{G}^{k}_{c}$.

A hallucination is considered extrinsic when knowledge is injected which is
not authentically captured by $\mathcal{G}^{k}_{c}$. However, the injected
knowledge is similar to the expected entity. Identifying this type of
hallucination can be challenging due to the high similarity between the
injected and gold knowledge. For example, in Figure 8 the dialogue sample
contains an extrinsic-soft hallucination as the entity in response – "Steven
Spielberg" is similar to "Christopher Nolan", and it is not supported within
1-hop sub-graph.

  3. (b)

(Extrinsic-Hard). An extrinsic-hard hallucination corresponds to an utterance
that brings a new span of text which is different from the expected span and
does not correspond to a valid triple in $\mathcal{G}^{k}_{c}$.

An extrinsic-hard hallucination occurs when injected knowledge is dissimilar
to the expected entity and is not supported within the 1-hop sub-graph. It is
easier to detect extrinsic-hard than extrinsic-soft as the entities are
fundamentally different from the entities present in the 1-hop sub-graph.
However, the entity type is retained, like an entity with a type "person" will
be replaced by the same type of entity. Figure 8 shows an example of
extrinsic-hard hallucination, where the golden entity "Christopher Nolan" is
replaced by a different category of entity, "Joe Biden", but the type of
entity is retained.

  4. (c)

(Extrinsic-Grouped). An extrinsic-grouped hallucination corresponds to an
utterance that brings a new span of text which is different from the expected
span but is of a specific predefined type and does not correspond to a valid
triple in $\mathcal{G}^{k}_{c}$.

Like an extrinsic-hard hallucination, extrinsic-grouped hallucination
introduces an entity that is functionally different from the original entity
and not supported by the 1-hop sub-graph. The only difference is that the
corrupted entity is not of the same type; instead, it is replaced by an entity
of a similar type, defined in Table 10. For example, Figure 8 shows
"Christopher Nolan" which is of type "person" is replaced by "Warner Bros." of
type "organization". Here, the types "person" and "organization" are placed in
the same group.

Group | Definition | Groups
---|---|---
1 |  | A person, organization, political party, or part
---
of a religious group can be related to each other.
"PERSON", "ORG", "NORP
2 |  | Location, building, airports, infrastructure
---
elements, countries, cities, and states can be interrelated
"LOC", "GPE", "FAC"
3 | A product, work of art, or law can be interrelated. | "PRODUCT", "WORK_OF_ART", "LAW"

Table 10: Defined groups for extrinsic-grouped hallucination Figure 9:
Intrinsic Hallucination

  5. (d)

(Intrinsic-Soft). An intrinsic-soft hallucination corresponds to an utterance
that misuses any triple in $\mathcal{G}^{k}_{c}$ such that there is no direct
path between the entities, but they are similar to each other.

Intrinsic hallucinations occur when the KG triples are misused, especially in
intrinsic-soft hallucination an entity is selected from $\mathcal{G}^{k}_{c}$
which is very similar or closely related to the original entity. For example,
in Figure 9, "Christopher Nolan" is replaced with "The Dark Knight Rises"
which is retrieved from the 1-hop sub-graph and has close relation with the
original entity "Christopher Nolan".

  6. (e)

(Intrinsic-Hard). An intrinsic-hard hallucination corresponds to an utterance
that misuses any triple in $\mathcal{G}^{k}_{c}$ such that there is no direct
path between the entities, and they are not related in any form.

Similar to intrinsic-soft hallucination, it also misuses the information in KG
triples. However, the similarity of the corrupted entity with the original
entity is relatively tiny. For example, in Figure 9, "Christopher Nolan" is
replaced with "United States of America". Although the corrupted entity is
drawn from $\mathcal{G}^{k}_{c}$, it is very different from the original
entity.

  7. (f)

(Intrinsic-Repetitive). An intrinsic-repetitive hallucination corresponds to
an utterance that either misuse [SBJ] or [OBJ] in $\mathcal{G}^{k}_{c}$ such
that there is no direct path between the entities but the entity has
previously occurred in conversational history..

An entity from the conversational history is often repeated in the current
utterances, which corresponds to intrinsic-repetitive hallucination. Here, an
entity from the history which also occurs in $\mathcal{G}^{k}_{c}$ and of high
relatedness, is swapped with the original entity. Figure 9 shows "Batman
Begins" which is supported by $\mathcal{G}^{k}_{c}$ is replaced with
"Christopher Nolan".

Figure 10: History Corrupted Hallucination

  8. (g)

(History Corrupted- Intrinsic/ Extrinsic). A history
corrupted(intrinsic/extrinsic) hallucination corresponds to an utterance
subjected to intrinsic or extrinsic hallucination influenced by hallucinated
entities in conversational history.

Sometimes conversational agents are driven into a perplexed state, and we can
witness hallucinations in most turns. So, this hallucinated history can
trigger hallucination in the current utterance. This phenomenon can be seen
both in extrinsic and intrinsic forms of hallucination. Figure 10 depicts
extrinsic/intrinsic hallucination occurring in history – "The Dark Knight" is
changed to "The Dark Knight Rises" for intrinsic hallucination; similarly,
"The Dark Knight" is changed to "Spider-Man" for extrinsic hallucination.
Hallucinations in the current utterance happen as described in previous
sections.

##  Appendix B AMT Instructions

We present the screenshot of the annotation interface in Figure 12, 12 and 13.
Workers were paid an average of $7-8 per hour across all tasks. We agree that
this annotation process has a high learning curve. Even workers with high
approval rates made errors in the initial rounds of annotation. A graduate
computer science student manually verified randomly selected samples and
provided feedback to the workers. Feedback was given to the workers,
especially when they selected the same answers for ten consecutive HITS. After
sending feedback three times, all spammed HITS were discarded.

Figure 11: Annotation interface for human feedback analysis(Instructions, part
1) Figure 12: Annotation interface for human feedback analysis(Instructions,
part 2) Figure 13: Annotation interface for human feedback analysis(example
annotation, workers were ask to find up to 3 spans if hallucinations are found
in the data)

##  Appendix C OpenDialKG

We use OpenDialKG Moon et al. (2019), a crowded-sourced English dialogue
dataset where two workers are paired together to chat about a particular
topic. The first speaker is requested to start the conversation about a given
entity. The second speaker is assigned to write an accurate response based on
facts extracted from an existing KG, Freebase Bast et al. (2014). The facts
represent paths from the KG that are either 1-hop or 2-hop from the initial
entity. Once the second speaker responds, the first speaker continues
discussing the topic engagingly, and new multi-hop facts from the KG are shown
to the second speaker. The dialogue can be considered as traversing multiple
paths in the KG. However, not all utterances within the same conversation are
grounded on facts from the KG. The second speaker can decide not to select a
path from the KG to form an answer and instead forms a "chit-chat" response.
Overall, the dataset consists of four domains: movie, music, sport, and book,
where each second speaker’s utterance is annotated with paths from the KG. The
KG corresponds to an extensive subgraph extracted from Freebase with $\sim$
1.2M triples (subject, predicate, object), $\sim$ 101k distinct entities, and
1357 distinct relations. We use 77,430 data points in the dataset for
constructing FADE.

##  Appendix D Perturbation Hyper-parameters

###  D.1 Search Index Details

We use Solr in case of extrinsic hallucination. We use the BM25 index, defined
by the class solr.BM25SimilarityFactory. We manually labeled 50 data
points(for the entity type PERSON) for tuning the indexes through grid search.
Grid-search conditions were as follows: b was varied from 0.3 to 0.9 with a
step of 0.1 and k1 was varied from 0.8 to 2.0 with a step of 0.2. Following
grid search, an optimum MAP score of 0.789 was found, with b = 0.9 and k1=
1.6. For the dynamic indexes that were created in the case of intrinsic
hallucination, we use the python library
https://github.com/dorianbrown/rank_bm25 with default configurations.

###  D.2 Free parameter & $\beta$ optimization

We use a free term weight parameter($\varepsilon$) in intrinsic hallucination
to represent the queries and nodes. Similar to extrinsic hallucination we
manually annotated 50 data-points and ran grid search for
$\varepsilon\in\\{10^{-i},2\times 10^{-i};i\in\\{1,5\\}\\}$, and found
$\varepsilon=2\times 10^{-4}$ to be the optimum value. We used the same
technique for optimizing $\beta$, and the search space ranged from 0.1 to 0.7
with a step of 0.05.

###  D.3 KG embeddings

We follow the same approach Dziri et al. (2021a) for generating the KG
embeddings. OpenDialKG triples are also represented using a textual term
called "render". For the triples containing this term, we pass it through to
GPT2 and then extract hidden state representations for each entity’s word
piece and finally obtain a final representation by applying a MaxPool over the
hidden representations. For entity mentions not described in “render”, we get
their representations directly from the last hidden states in GPT2.

###  D.4 Mixing Ratios

Mixing ratios for creating the mixed datasets are defined in Table 11.
Perturbed and non-perturbed samples are drawn randomly from component
datasets.

Dataset Type | Ext-Soft(%) | Ext-Hard(%) | Ext-Grp(%) | Int-Soft(%) | Int-Hard(%) | Int-Rep(%) | HC-Ext(%) | HC-Int(%) | N-Halluc(%)
---|---|---|---|---|---|---|---|---|---
Observed | 12.495 | 6.4425 | 1.04 | 0.92 | 1.025 | 1.7 | 2.4575 | 1.4575 | 72.4625
Balanced | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 50
Extrinsic+ | 12.5 | 9.375 | 9.375 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 37.5
Intrinsic+ | 6.25 | 6.25 | 6.25 | 9.375 | 9.375 | 9.375 | 6.25 | 6.25 | 40.625

Table 11: Mixing ratios for different datasets

##  Appendix E Implementation Details

The utterance and token level classifier are implemented using the Pytorch
Huggingface Transformers library Wolf et al. (2020). The following
configuration were found to be best performing for each models, as shown in
Table 12, 13, 14 and 15. The models were trained in a single NVIDIA A5000 GPU,
the average running time for the base models were 2.5 hours, and for the large
model was $\sim$ 5 hours.

Hyperparameter | Value
---|---
train_batch_size | 12
gradient_accumulation_steps | 2
num_train_epochs | 4(Token)/10(Utt)
weight_decay | 0.01
warmup_proportion | 0.1
learning_rate | 1e-5
adam_epsilon | 1e-8
max_grad_norm | 1
eval_batch_size | 18

Table 12: RoBERTa-base hyper parameters

Hyperparameter | Value
---|---
train_batch_size | 12
gradient_accumulation_steps | 2
num_train_epochs | 4(Token)/10(Utt)
weight_decay | 0.01
warmup_proportion | 0.1
learning_rate | 2e-5
adam_epsilon | 1.5e-8
max_grad_norm | 1
eval_batch_size | 18

Table 13: RoBERTa-large hyper parameters

Hyperparameter | Value
---|---
train_batch_size | 12
gradient_accumulation_steps | 2
num_train_epochs | 4(Token)/10(Utt)
weight_decay | 0.01
warmup_proportion | 0.1
learning_rate | 5e-5
adam_epsilon | 1e-8
max_grad_norm | 1
eval_batch_size | 18

Table 14: BERT-base-uncased hyper parameters

Hyperparameter | Value
---|---
train_batch_size | 12
gradient_accumulation_steps | 2
num_train_epochs | 4(Token)/10(Utt)
weight_decay | 0.01
warmup_proportion | 0.1
learning_rate | 5e-5
adam_epsilon | 1e-8
max_grad_norm | 1
eval_batch_size | 18

Table 15: XLNet-base hyper parameters

##  Appendix F Supplementary results

We report metrics for all the models trained using 25% of the dataset, for
component datasets in Table 16 and mixed datasets in Table 17.

Dataset | Best Model | Token Level | Utterance Level
---|---|---|---
F1 | P | R | F1 | P | R | G-Mean | BSS | AUC
extrinsic_hard | roberta-base | 0.70613382 | 0.68956357 | 0.72352004 | 0.86181139 | 0.83985441 | 0.88494727 | 0.93029609 | 0.03277357 | 0.93145803
extrinsic_grouped | roberta-base | 0.7986706 | 0.77534593 | 0.82344214 | 0.90499405 | 0.89090483 | 0.91953606 | 0.93487266 | 0.0589842 | 0.93500056
intrinsic_hard | roberta-base | 0.84409519 | 0.84717262 | 0.84104004 | 0.90789771 | 0.92741563 | 0.8891844 | 0.93192336 | 0.04522725 | 0.9329505
intrinsic_soft | roberta-base | 0.78797921 | 0.80193163 | 0.774504 | 0.87102229 | 0.90540109 | 0.83915877 | 0.90255779 | 0.06217348 | 0.90495271
intrinsic_repetitive | roberta-base | 0.82702178 | 0.82759578 | 0.82644857 | 0.88005638 | 0.89506881 | 0.86553923 | 0.92305012 | 0.03146957 | 0.92496078
intrinsic_history_corrupt | roberta-base | 0.83406626 | 0.82763636 | 0.84059684 | 0.90857229 | 0.92340555 | 0.89420804 | 0.93381877 | 0.04511612 | 0.93469609
extrinsic_history_corrupt | roberta-base | 0.72010547 | 0.71212516 | 0.72826667 | 0.87400219 | 0.85486834 | 0.89401217 | 0.93612638 | 0.02971357 | 0.93711831
extrinsic_soft | roberta-base | 0.60045426 | 0.60811376 | 0.59298532 | 0.72017689 | 0.74873563 | 0.69371672 | 0.81231271 | 0.09344656 | 0.82245014
extrinsic_hard | bert-base-uncased | 0.71146832 | 0.72259569 | 0.70067846 | 0.88285121 | 0.88299233 | 0.88271013 | 0.93232489 | 0.02705296 | 0.93371925
extrinsic_grouped | bert-base-uncased | 0.80688364 | 0.8056026 | 0.80816875 | 0.91302235 | 0.9180408 | 0.90805848 | 0.93577473 | 0.05285693 | 0.93619772
intrinsic_hard | bert-base-uncased | 0.83328471 | 0.82308025 | 0.84374538 | 0.91416629 | 0.92395896 | 0.90457903 | 0.93917074 | 0.04259417 | 0.93983215
intrinsic_soft | bert-base-uncased | 0.75277325 | 0.79087205 | 0.71817644 | 0.85483616 | 0.91836735 | 0.79952621 | 0.88349437 | 0.06794858 | 0.88790364
intrinsic_repetitive | bert-base-uncased | 0.7481198 | 0.71392596 | 0.78575388 | 0.84134941 | 0.82295256 | 0.86058758 | 0.91436157 | 0.04330141 | 0.9160416
intrinsic_history_corrupt | bert-base-uncased | 0.82318199 | 0.8229997 | 0.82336435 | 0.90891209 | 0.9316067 | 0.8872969 | 0.93164021 | 0.04459424 | 0.93274826
extrinsic_history_corrupt | bert-base-uncased | 0.67029785 | 0.69294369 | 0.64908533 | 0.87358552 | 0.88214169 | 0.86519372 | 0.92312672 | 0.02886461 | 0.9250663
extrinsic_soft | bert-base-uncased | 0.64089366 | 0.6922167 | 0.59665579 | 0.7480315 | 0.81958894 | 0.68796592 | 0.81616138 | 0.08033967 | 0.82810534
extrinsic_hard | xlnet-base-cased | 0.72115512 | 0.71982018 | 0.72249502 | 0.8736255 | 0.8712651 | 0.87599872 | 0.92800607 | 0.02926739 | 0.92954989
extrinsic_grouped | xlnet-base-cased | 0.78452923 | 0.77288925 | 0.79652518 | 0.89920345 | 0.8915677 | 0.90697112 | 0.92895654 | 0.06212166 | 0.92922301
intrinsic_hard | xlnet-base-cased | 0.84443122 | 0.85082459 | 0.83813322 | 0.90878914 | 0.92875867 | 0.88966027 | 0.93238499 | 0.04477944 | 0.93341088
intrinsic_soft | xlnet-base-cased | 0.76722735 | 0.80484632 | 0.73296801 | 0.85379657 | 0.90941058 | 0.80459259 | 0.88491991 | 0.06892207 | 0.88892969
intrinsic_repetitive | xlnet-base-cased | 0.7941989 | 0.79135701 | 0.79706127 | 0.86978508 | 0.88154897 | 0.85833102 | 0.91820183 | 0.03428001 | 0.9202899
intrinsic_history_corrupt | xlnet-base-cased | 0.83667247 | 0.82269807 | 0.85112982 | 0.91298209 | 0.91864812 | 0.90738552 | 0.9396723 | 0.04337198 | 0.94024672
extrinsic_history_corrupt | xlnet-base-cased | 0.72378159 | 0.72354039 | 0.72402294 | 0.88100942 | 0.87862377 | 0.88340807 | 0.93239789 | 0.02749991 | 0.93375627
extrinsic_soft | xlnet-base-cased | 0.60896216 | 0.63207547 | 0.58747961 | 0.73844753 | 0.79296016 | 0.69094782 | 0.81535862 | 0.08484401 | 0.82655921

Table 16: All models benchmark (numbers in fractions) for component datasets,
models trained on 25% of the total dataset.

Dataset | Best Model | Token Level | Utterance Level
---|---|---|---
F1 | P | R | F1 | P | R | G-Mean | BSS | AUC
balanced | roberta-base | 0.73405875 | 0.68751809 | 0.78735795 | 0.882424 | 0.83853553 | 0.93116042 | 0.86213807 | 0.131385 | 0.86469621
observed | roberta-base | 0.62554537 | 0.59004757 | 0.66558773 | 0.77904114 | 0.73266454 | 0.83168565 | 0.85077041 | 0.14126728 | 0.85098938
extrinsic_plus | roberta-base | 0.74849152 | 0.71339648 | 0.78721816 | 0.90921175 | 0.87804878 | 0.94266814 | 0.84332203 | 0.12278872 | 0.84855698
intrinsic_plus | roberta-base | 0.75045075 | 0.71112613 | 0.79437919 | 0.90157054 | 0.86518353 | 0.94115257 | 0.84511316 | 0.12778319 | 0.85001331
balanced | bert-base-uncased | 0.6570643 | 0.57930535 | 0.7589345 | 0.85119497 | 0.78285516 | 0.9326075 | 0.81309735 | 0.17265032 | 0.82075474
observed | bert-base-uncased | 0.59965325 | 0.52847854 | 0.6929832 | 0.76124302 | 0.67629046 | 0.87060443 | 0.84589508 | 0.16352531 | 0.84624573
extrinsic_plus | bert-base-uncased | 0.72993044 | 0.663004 | 0.81188563 | 0.90179749 | 0.84940317 | 0.96108049 | 0.8086632 | 0.1365238 | 0.82074909
intrinsic_plus | bert-base-uncased | 0.71653573 | 0.65640721 | 0.78879093 | 0.89301716 | 0.84373548 | 0.94841293 | 0.82126564 | 0.14130552 | 0.82978853
balanced | xlnet-base-cased | 0.71863497 | 0.66214437 | 0.78566356 | 0.87222741 | 0.81850039 | 0.93350331 | 0.84619893 | 0.14481173 | 0.85028143
observed | xlnet-base-cased | 0.63436089 | 0.57976023 | 0.70031519 | 0.77706573 | 0.71053723 | 0.85733951 | 0.8540217 | 0.14730018 | 0.85402812
extrinsic_plus | xlnet-base-cased | 0.75593757 | 0.7079124 | 0.81095307 | 0.90747949 | 0.86768256 | 0.95110254 | 0.83209459 | 0.12649216 | 0.8395401
intrinsic_plus | xlnet-base-cased | 0.74488988 | 0.68995602 | 0.80932808 | 0.90141776 | 0.85748704 | 0.95009285 | 0.83869733 | 0.129222 | 0.84522772

Table 17: All model benchmark (numbers in fractiom) for mixed datasets, models
trained on 25% of the total dataset.