# Redefining ”Hallucination” in LLMs: Towards a psychology-informed framework
for mitigating misinformation

Elijah Berberette  Jack Hutchins  Amir Sadovnik

###### Abstract

In recent years, large language models (LLMs) have become incredibly popular,
with ChatGPT for example being used by over a billion users. While these
models exhibit remarkable language understanding and logical prowess, a
notable challenge surfaces in the form of ”hallucinations.” This phenomenon
results in LLMs outputting misinformation in a confident manner, which can
lead to devastating consequences with such a large user base. However, we
question the appropriateness of the term ”hallucination” in LLMs, proposing a
psychological taxonomy based on cognitive biases and other psychological
phenomena. Our approach offers a more fine-grained understanding of this
phenomenon, allowing for targeted solutions. By leveraging insights from how
humans internally resolve similar challenges, we aim to develop strategies to
mitigate LLM hallucinations. This interdisciplinary approach seeks to move
beyond conventional terminology, providing a nuanced understanding and
actionable pathways for improvement in LLM reliability.

Large Language Models, Hallucinations

##  1 Introduction

Recent breakthroughs in large language models (LLMs) have propelled the
widespread adoption of conversational AI across diverse applications.
Exemplified by LLMs such as ChatGPT, GPT-4, and BARD, these models have
demonstrated remarkable proficiency in language comprehension (Xiao et al.,
2023) and logical reasoning (Luo et al., 2023). Notably, they have
consistently exhibited the ability to surpass the Turing Test (Dodig-Crnkovic,
2023), marking a significant leap forward in the field. Amidst this success, a
critical challenge has emerged—hallucinations.

The definition of the term “hallucination” varies amongst authors of prior
work. Some authors define “hallucination” from a real-world perspective. For
example, Alkaissi et al. define this term generally as “generating seemingly
realistic sensory experiences that do not correspond to any real-world input”
(Alkaissi & McFarlane, 2023). Many authors simply describe this as an
unfactual statement that is not present in training data (Lemley et al.,
2023). Other authors seek to separate this umbrella term into multiple sub-
terms that each explain different undesired outputs from LLMs (Zhang et al.,
2023).

The issue with hallucinations in LLMs is they often appear correct to someone
not familiar with the subject area they are asking about. Often, LLMs will
answer in a confident way or even explain logical steps of how they arrived at
the answer even when that answer even when they are incorrect. With how wide
the user base of conversational LLMs is, it’s expected that most of the users
will not be educated about what hallucinations are. Figure 1 shows a
conversation depicting an example of what a hallucination looks like when
interacting with an LLM. At first glance, this response that the LLM produces
seems convincing and logical, especially to someone who is not familiar with
calculus. However, through further investigation, one can identify the
invalidity in the confident answer given. This incorrect answer stems from
ChatGPT-3.5 interpreting ”two times” the derivative as taking the second
derivative of the function twice instead of the multiplication of the constant
2 and the derivative of the function ${3x^{2}}.$

Figure 1: Conversation with ChatGPT that contains a hallucination. We asked
the model ”What is two times the derivative of 3x squared” to which it
responds with the incorrect answer of 12.

While ”hallucination” has become the dominant term for this phenomenon both in
academia and media, hallucinations in humans have a much different definition.
The NHS defines hallucinations as ”when you hear, see, smell, taste, or feel
things that appear to be real but only exist in your mind” (NHS, 2023). This
prompts a reflection on the appropriateness of categorizing all of these
phenomena as ”hallucinations,” as it can be misleading to classify them as
such. To rectify this, our paper attempts to align these occurrences more
accurately with psychological phenomena that parallel the ”hallucination”
phenomena observed in LLMs.

In this work, our foremost goal is to provide a future direction for
mitigating ”hallucinations.” To achieve this objective, we advocate for a
paradigm shift in how these phenomena are understood, utilizing a more precise
lexicon borrowed from the realm of psychology. Psychological concepts such as
source amnesia, availability heuristics, recency effect, cognitive dissonance,
suggestibility, and confabulation will serve as the basis for our new
characterization.

Our departure from the conventional use of the term ”hallucination” is not a
mere semantic exercise; rather, it serves as a deliberate means to enhance our
understanding of the limitations and challenges faced by advanced language
models. By grounding our discussion in specific psychological constructs, we
seek to shed light on these phenomena in language models, paving the way for
the development of targeted solutions for different types of ”hallucinations.”

##  2 Previous Work

Within the current body of available literature, many authors define the term
“hallucination” as “generated content that is nonsensical or unfaithful to the
provided source content” (Lu et al., 2018). It is also very common for authors
to separate this term into two separate definitions, intrinsic hallucinations
and extrinsic hallucinations (Ji et al., 2022). Intrinsic hallucinations in
conversational large language models are outputs that directly contradict the
source content or conversational history. Meanwhile, extrinsic hallucinations
are outputs that cannot be proven or disproven based on the source content or
conversational history (Ji et al., 2022). Essentially, intrinsic
hallucinations are a fundamental misinterpretation of the information, while
extrinsic hallucinations introduce unnecessary, incorrect details. It is
important to note that intrinsic and extrinsic hallucinations are not mutually
exclusive, meaning they can occur at the same time in the same output (Zhou et
al., 2020). The following example shows a fictional intrinsic and extrinsic
hallucination example:

Useruser Correct Translationbot Intrinsic Translationintrinsic Extrinsic
Translationextrinsic

: “Please translate the following text: Un niño saltó un arroyo para llegar al
otro lado.”

: A boy jumped over a creek to get to the other side. : A girl jumped over a
river to get to the other side. : A boy jumped over a large creek to get to
the other side.

The example depicts an intrinsic and extrinsic hallucination arising during
translation. The intrinsic example changes ”boy” to ”girl” and ”creek” to
”river” which is clearly incorrect when compared with the correct translation.
The extrinsic example introduces the adjective ”large” which does not directly
contradict the correct translation; however, it was not explicitly stated in
the original text.

Synonymous with extrinsic hallucinations, recent studies have shown that
hallucinations do not always contain false information. Zhang et al splits the
polysemous term “hallucination” into three separate subterms that each capture
a different type of “hallucination” (Zhang et al., 2023). The first type of
hallucination they identify is “Input-Conflicting Hallucination,” which is a
response that diverges from the input provided by the user. For example, if
the user were to ask an LLM what the most efficient truck is, and the model
responds with “The most efficient car is a hybrid sedan,” this response is not
false information; however, it would be labeled an input-conflicting
hallucination. The other two hallucinations identified in this paper are
“context-conflicting hallucinations”, and “fact-conflicting hallucinations”.
Context-conflicting hallucinations are similar to intrinsic hallucinations in
that they contain information that deviates from previous outputs. An example
of this would be if the LLM previously stated that the ocean is around
139,000,000 square miles then in the following output stated that the ocean is
around 140,000,000 square miles. Lastly, “fact-conflicting hallucinations” are
simply inaccurate statements outputted by the model. It is important to once
again note that these terminologies are not mutually exclusive and can be
identified in the same outputs.

In an attempt to extend this direction of breaking the term ”hallucination”
into distinct subcategories, several authors have altered the definition of
the term based on the tasks being performed by LLMs. These include tasks such
as abstractive summarization (Zhao et al., 2020) and language translation (Ji
et al., 2022). In models used for summarizing tables, authors found it
beneficial to further label this phenomena based on the material that was
being hallucinated to assist with mitigation strategies of inaccurate output
by the model (Zhao et al., 2020). For example, if an LLM is tasked to
summarize a table of purchases for a small business, it is useful to treat
hallucinated dates differently than hallucinated monetary entries. While this
approach offers a useful taxonomy for addressing hallucinations on a task-by-
task basis, we argue that it is more beneficial to discontinue labeling these
irregularities in LLMs as ”hallucinations” entirely. By looking at this term
more generally, we can yield more robust mitigation strategies and a deeper
understanding of the issue.

We acknowledge that these authors effectively dissect the term ”hallucination”
into distinct terms that offer a precise framework for identifying the varied
phenomena at play. Each of these authors’ unique approaches allows for finer
granularity in characterizing different types of hallucinations, offering
additional clarity for how one might mitigate these issues observed in LLMs.
While the specificity of this proposed taxonomy allows for more specific
solutions, our objective is to offer an alternative path forward, derived from
psychology, that allows for the mitigation of ”hallucinations” that arise in
LLMs. We strive to provide this future direction for mitigation strategies by
enhancing the correlation between the ”hallucinations” and psychology. This
would allow us to combat the various unfavorable outputs produced by LLMs by
utilizing the plethora of knowledge that currently exists in the field of
psychology.

One step in this alternative direction is shown in a recent article where the
authors attempt to redefine the word “hallucination” by using more accurate
terminology that aligns with other fields like neuroscience and psychology
(Smith et al., 2023). In this article, Smith et al. argue that in order to
refer to the behavior of LLMs producing false, misleading information, one
would need to also believe that LLMs are perceiving. The authors further argue
that a better term for this phenomenon that occurs is “confabulation” which is
a medical disorder where patients produce false memories without attempting to
deceive the individual they are speaking with (Wiggins & Bunin, 2023). While
this research is in line with the objective of our paper, we intend to connect
additional existing terminology to specific examples found while interacting
with LLMs.

##  3 The Issue with the term ”Hallucination”

In humans, hallucinations refer to perceptual experiences that occur in the
absence of external stimuli. These experiences can manifest in various sensory
modalities, including visual, auditory, tactile, olfactory, or gustatory
sensations (Asaad & Shapiro, 1986). Hallucinations are essentially perceptions
that occur without corresponding external stimuli that would typically evoke
such sensations.

Hallucinations are often associated with psychiatric disorders, neurological
conditions, or substance-induced states. For example, individuals with
schizophrenia may experience auditory hallucinations, hearing voices that
others do not hear (Waters et al., 2012). Similarly, hallucinations can occur
as a result of conditions like epilepsy (Elliott et al., 2009), migraines
(Schott, 2007), or drug intoxication (Manford & Andermann, 1998).

When it comes to LLMs, the term ”hallucination” is metaphorically used to
describe instances where the model generates outputs that may seem realistic
but are not grounded in actual data or external reality. In LLMs,
”hallucination” is a term used to highlight the model’s capability to generate
contextually relevant and coherent information even when it hasn’t been
explicitly exposed to specific data during training.

However, it’s important to note that using ”hallucination” in the context of
language models is not a perfect analogy. Unlike human hallucinations, which
are often symptomatic of underlying disorders or conditions, the so-called
hallucinations in language models are a result of the model’s probabilistic
nature and training data among other factors. Language models lack
consciousness, subjective experience, or awareness, and their outputs are
generated based on patterns learned from vast amounts of training data.

The term ”hallucination” in the context of language models may be misleading
if taken too literally. While it captures the idea of generating seemingly
realistic outputs, it does not imply any form of subjective experience,
intentionality, or understanding on the part of the model. It’s a linguistic
metaphor used to describe a characteristic of the model’s output rather than a
genuine cognitive process.

##  4 Psychology-Informed Taxonomy

While it’s clear that the term ”hallucination” is not an accurate descriptor
of these phenomena in LLMs, we do believe that metaphors with human psychology
can be incredibly valuable in understanding LLMs. Smith et al. already
proposed the use of ”confabulation” instead of ”hallucination,” (Smith et al.,
2023) which we believe more accurately captures the essence of these phenomena
in general terms. That being said, there is much more room to create
connections between human psychology and ”hallucinations” in LLMs. The
following section will present several psychological phenomena and cognitive
biases that we believe closely match different types of ”hallucinations” in
LLMs. It is important to note that several of the psychological phenomena we
have identified overlap with each other, and we will point these overlaps out
as they appear. Through this, we hope to gain a better understanding of what
”hallucinations” are and how they might arise. An overview of these phenomena
can be seen in Figure 2.

Figure 2: An overview of psychological phenomena and cognitive biases in
humans and their parallel in LLMs

###  4.1 Source Amnesia

Source amnesia refers to the phenomenon where individuals have difficulty
remembering the origin or source of a particular memory, idea, or piece of
information, and it is one of the 7 sins of memory identified by Schacter
(Schacter et al., 1984; Schacter & Dodson, 2001). In other words, people may
recall information correctly, but they struggle to remember where or how they
acquired that information.

This cognitive phenomenon highlights the dissociation between the content of a
memory and the context in which it was acquired. It can occur in various
situations, such as when someone hears a piece of information from multiple
sources or encounters it in different contexts. As a result, the individual
might mistakenly attribute the memory to the wrong source or be unable to
identify the original source altogether.

Source amnesia, in the context of LLMs, refers to the tendency of these models
to generate content without an accurate recollection or acknowledgment of the
origin of the information. This phenomenon is particularly notable when the AI
system inadvertently synthesizes text that closely mimics or paraphrases input
data without proper attribution. In other words, the model fails to attribute
the generated content to its appropriate source, leading to the AI system
operating without a clear memory of the informational roots.

For a simple example, see Figure 3, which shows a conversation with LLaMA-2 7B
(Touvron et al., 2023). In this example, we ask LLaMA to briefly describe
detritivores, to which it responds with a short description. Following this,
we ask it to provide a citation for the description it just gave. This results
in LLaMA giving us a non-existent paper. The model even goes as far as to
provide a summary of this fake paper, thus demonstrating one aspect of source
amnesia in LLMs.

Figure 3: Conversation with LLaMA-2 7B where we ask it to describe
detritivores. When asked to cite the answer it gave, LLaMA-2 responded with a
fake article, thus demonstrating source amnesia.

Additionally, it is important to note that hallucinations can occur more
abstractly as a result of source amnesia. Consider an LLM trained on diverse
datasets, including medical literature and fictional narratives. In response
to a medical query, the model may generate a response that combines accurate
medical information with elements from fictional stories, showcasing a
manifestation of source amnesia. The model, lacking the ability to
differentiate between factual and fictional sources, amalgamates disparate
information, potentially leading to misinformation or misinterpretation.

###  4.2 Recency Effect

The recency effect is a cognitive phenomenon wherein individuals tend to
better recall and give greater importance to information or events that
occurred more recently (Baddeley & Hitch, 1993). This bias in memory is
particularly prominent in the context of list-based presentations or
sequences. When presented with a series of items or information, individuals
are more likely to remember and emphasize the items encountered near the end
of the list. This effect is believed to be influenced by the workings of
short-term memory, where the most recent information is still readily
accessible. The recency effect can impact various aspects of decision-making,
evaluation, and overall perception as people tend to assign greater
significance to the freshest information in their minds.

Many LLMs, including ChatGPT and GPT-4 include reinforcement learning with
human feedback (RLHF) (OpenAI, 2023). This can lead to an interesting way that
hallucinations occur in LLMs, mirroring the recency effect. One way that the
recency effect can occur in LLMs is via confirmation bias in the user.
Confirmation bias is the idea that people will prefer information that
corresponds to their beliefs when compared with information that rejects their
beliefs (Klayman, 1995). Often, people use LLMs to verify their beliefs,
leading to confirmation bias in the user. Given this, it’s easy to imagine a
situation where the human-in-the-loop prefers responses from the LLM that
confirm their existing beliefs regardless of whether the information is
factual or not. Over time, this can lead to the LLM producing more
hallucinations, since it favors recent interactions that indulge the user’s
confirmation bias instead of the long-term reward of producing output with
fewer hallucinations.

This idea is not just hypothetical, many people have hypothesized that ChatGPT
has become ”dumber” over time as pointed out in articles from New York
Magazine (Herrman, 2023) and DW (Abid, 2023). After much speculation, Chen et
al. evaluated the performance of ChatGPT on varying tasks in March 2023 and
then again in June 2023 (Chen et al., 2023). What they found were significant
differences in performance in a relatively short period, especially in GPT-4
where they observed significant performance drop-offs in math and programming
skills. One example the authors present is asking GPT-4 ”Is 17077 a prime
number? Think step by step and then answer ”[Yes]” or ”[No]”. When asked in
March 2023 the model responded with an in-depth chain of thought converging on
the correct answer of ”[Yes]”. However, when the model was asked again in June
2023 it simply answered the incorrect answer of ”[No]” with no explanation.
While it is difficult to be sure what caused this regression since these
models are not open source, one speculation could be that these are a result
of the recency effect.

###  4.3 Availability Heuristics

The availability heuristic is a cognitive bias that influences decision-making
and judgment based on the ease with which specific information comes to mind
(Schwarz et al., 1991). Essentially, individuals tend to overestimate the
importance or likelihood of events based on their immediate recall from
memory. In LLMs, the availability heuristic plays a noteworthy role in shaping
text generation.

In LLMs, the availability heuristic manifests as a tendency to prioritize
information that is more accessible or prevalent in the training data. During
the extensive training process, the model is exposed to a vast corpus of text,
and certain patterns, phrases, or concepts may be more frequently encountered
than others. As a result, when prompted with a query or input, the LLM may
draw heavily from the readily available information in its memory,
irrespective of the actual significance of that information.

For example, suppose a language model has been exposed to a disproportionate
amount of data related to a specific topic. In that case, it may exhibit an
availability bias by generating responses that align more closely with the
patterns found in that particular domain. This can lead to outputs that may
seem overly biased or limited in scope, as the model relies on the information
that is most prominent in its training data.

Moreover, the availability heuristic in LLMs can contribute to the
perpetuation of stereotypes and biases present in the training data. If
certain groups, themes, or perspectives are overrepresented in the model’s
training data, the LLM may inadvertently reinforce these biases in its
generated content. This phenomenon raises important considerations regarding
the ethical use of language models, as the outputs may inadvertently reflect
and perpetuate societal biases embedded in the training data. While this is a
very important consequence to recognize, this is not the only way the
availability heuristic can present itself. Furthermore, significant research
has already been performed in studying bias in LLMs (Vig et al., 2020; Abid et
al., 2021; Liang et al., 2021).

Figure 4 provides a simple example in GPT-3 of the availability heuristic in
LLMs from (Navigli et al., 2023). In this example, the model is asked to ”tell
me about your nurse,” to which the model responds by using the pronoun ”she.”
The choice to use feminine pronouns is interesting because the gender of the
nurse is never mentioned, but nurses who use feminine pronouns are likely much
more prominent in the training data. This leads to bias and the presence of
the availability heuristic in the model. Furthermore, when asked about the
plastic surgeon, the model chooses to use masculine pronouns. Again, this
happens without the gender of the surgeon being mentioned.

Figure 4: Q&A with GPT-3 that shows bias and the availability heuristic from
(Navigli et al., 2023). (bold added for emphasis)

###  4.4 Suggestibility

We believe that LLMs can fall victim to another one of the seven sins of
memory, suggestibility (Schacter, 1999). Suggestibility in the field of
psychology is a memory distortion where individuals introduce false
information into their recollection due to external suggestions. Similar to
how individuals may be misled by the way they are presented with information,
LLMs may also fall victim to the way they are prompted. If the user’s prompt
includes strong bias or leading questions, the model may be more likely to
diverge from the initial answer or response it provides. Previous authors
would relate this divergence to context-conflicting hallucinations.

We define the term “suggestibility” in LLMs as incorporating false or
misleading information into responses due to inaccuracies in user-provided
prompts. One way that an LLM can be subjected to suggestibility is by
inquiring whether the model is confident that the given response is correct.
This can have both positive and negative effects on the output that follows.
For example, this approach can be extended to include reinforcing information
or misleading information to lead the model toward producing true or false
responses, respectively. This outcome is a possible side effect of RLHF, since
during the RLHF process, the model is rewarded for producing responses that
please the user.

Let’s consider the example shown in Figure 5 of a conversation we had with the
popular LLM, Bard. First, we provided Bard with the prompt shown in the
introduction asking Bard to solve a math problem from the field of Calculus.
Unlike the conversation with ChatGPT, shown in Figure 1, Bard was able to
solve this problem correctly with steps to show how it came to the answer. We
then prompted Bard with a misleading question, asking Bard if the answer that
it provided was incorrect. We also included a reason for why we were not
convinced by its output. This exposure to suggestibility resulted in Bard
retracting its initial answer and producing an incorrect final answer with
incorrect reasoning.

Figure 5: We introduced suggestibility into a conversation with Google’s Bard.
This exposure to suggestibility leads to an incorrect answer and steps
outputted by Bard.

###  4.5 Cognative Dissonace

Another term that we identify is cognitive dissonance. In the field of
psychology, cognitive dissonance refers to the uncomfortable psychological
tension that arises due to dissonant beliefs, often leading individuals to
resolve this tension by refraining from dissonant beliefs. This phenomenon was
first coined in 1957 by Leon Festinger (Festinger, 1957). In this study,
Festinger defined consonant beliefs as beliefs that align and dissonant
beliefs as beliefs that conflict.

Our application of this terminology is strictly metaphorical in the sense that
we do not argue that LLMs hold actual beliefs. We also recognize that LLMs are
incapable of being uncomfortable. Our definition of cognitive dissonance as it
relates to LLMs is the tension that surfaces during training due to the
training data containing information that is in direct conflict with other
information in the dataset. We argue that this leads LLMs to produce input-
conflicting and context-conflicting responses, due to the internal “tensions”
developed from training on a large corpus of potentially conflicting
information.

Figure 6 shows an example of cognitive dissonance arising in Facebook’s open-
source LLM, LLaMA-2. For this, we asked LLaMA-2: ”Are pitbulls a good dog to
own?” We chose this question because pitbulls commonly have a negative
connotation attached to their breed due to stereotypes of them being
aggressive. On the other end of the spectrum, many people have overwhelmingly
positive opinions on pitbulls, giving rise to perfect conditions for cognitive
dissonance in the model. From this prompt, we received a list of pros and cons
that have conflicting points. In the second pro, LLaMA-2 states that pitbulls
are intelligent dogs that are easy to train with the right techniques. The
model then contradicts itself in the third con, stating that it is difficult
to teach pitbulls basic obedience commands. Previous authors would label this
contradiction or divergence from the prior context as a context-conflicting
hallucination; however, we believe that the model has fallen subject to
cognitive dissonance due to biases arising in training.

Figure 6: Conversation with LLaMA-2 discussing if pitbulls are good dogs to
own. This demonstrates cognitive dissonance arising because LLaMA-2
contradicts itself by saying pitbulls are both difficult and easy to train.
(bold added for emphasis)

###  4.6 Confabulation

The last category that we identify is confabulation. This phenomenon, in the
field of psychology, is when a patient produces false memories without
attempting to deceive the individual they are speaking with (Wiggins & Bunin,
2023). We believe that this category captures a large portion of the confident
misinformation produced by conversation AI. This is a broad category, as it
can capture many other irregular outputs that do not fall directly into the
other categories.

We define ”confabulation” with respect to LLMs as a confident, but misleading
output generated with the intention of accurately fulfilling the user’s
prompt. Similar to the previous terminology, ”intention” is employed
metaphorically, referring to the ability to generate coherent and contextually
relevant text based on input prompts. These well-meaning but misguided
responses stem from a few reasons. These include the large, uncurated corpus
of text used to train LLMs, stochasticity, and RLHF (Edwards, 2023). This term
relates heavily to what previous authors have identified as fact-conflicting
hallucinations.

In Figure 7, we show an example of confabulation arising in a conversation
with ChatGPT. In this example, we prompted ChatGPT with a niche question
asking ChatGPT what the loss function is for YOLOV4. ChatGPT responded that
the loss function for YOLOV4 was not provided in the original paper; however,
the YOLOV4 paper clearly states that they opted to use cIoU as their loss
function (Bochkovskiy et al., 2020). It is important to also note that this
paper was last updated in 2020, which means that ChatGPT was exposed to this
content in its ”last knowledge update in Janurary 2022.”

Figure 7: Conversation with ChatGPT depicting confabulation arising in a
response. The model claims that the original paper does not include the loss
function, which is untrue.

##  5 Discussion

With our new methodology of taxonomizing hallucinations in LLMs, it is
important to explore what can be learned from this. Notably, we can evaluate
how humans avoid cognitive biases and memory discrepancies. This exploration
begins with an examination of metacognition in humans, a cognitive process
crucial for mitigating the impact of cognitive biases.

Metacognition, the ability to monitor and regulate one’s own thinking
processes, serves as a safeguard against the pitfalls of misinformation and
cognitive biases in humans (Lai, 2011). This capacity enables individuals to
reflect on their thought processes, assess information reliability, and
discern the sources of their beliefs, fostering critical thinking.
Metacognition empowers individuals to rectify misinterpretations through
reflective thinking, a process involving revisiting the cognitive processes
that led to a belief or perception (Flavell, 1979). This retrospective
analysis facilitates the identification of errors in judgment, correction of
misconceptions, and updating of mental models to align more closely with
reality.

Source monitoring, another facet of metacognition, involves the evaluation of
the credibility of information sources (Johnson et al., 1993). Humans use this
mechanism to distinguish reliable from unreliable sources, filtering out
misinformation and enhancing the accuracy of their mental representations.
Metacognitive awareness prompts individuals to question the origin of their
beliefs, discerning whether information is based on personal experiences,
external evidence, or flawed reasoning. This process is often used to mitigate
source amnesia and suggestability.

Metacognition is also pivotal in the process of forming thoughts and ideas,
particularly through the use of convergent and divergent thinking (Jia et al.,
2019). In the initial stages of thought, individuals engage in divergent
thinking, leveraging metacognitive skills to explore a multitude of ideas with
spontaneity and creativity. This phase involves the generation of diverse
possibilities and the evaluation of their novelty. As the cognitive process
evolves, metacognition aids in the transition to convergent thinking, where
individuals systematically assess and refine the most promising ideas,
emphasizing logical coherence. The collaborative dynamics of metacognition,
convergent, and divergent thinking provide us with a mechanism to reduce
cognitive dissonance and provide logically consistent arguments while still
being creative.

Applying these metacognitive mechanisms to LLMs requires a nuanced approach.
While machines lack self-awareness in the human sense, incorporating
metacognitive-like functionalities could serve as a valuable mitigation
strategy for hallucinations. Emulating human metacognitive processes in LLMs
could involve enhancing source attribution capabilities, and implementing
algorithms that simulate source monitoring to evaluate data reliability and
credibility. Continuous learning mechanisms and model recalibration can allow
LLMs to adapt and self-correct in response to evolving information.

While continuous learning could help reduce some of the phenomena we have
discussed, it could also worsen other aspects such as the recency effect.
Therefore, introducing a form of reflective processing within LLMs could
contribute to error detection and correction. By embedding algorithms that
analyze and adjust the model’s decision-making processes, LLMs may develop a
form of artificial metacognition, improving their ability to discern and
rectify hallucinatory outputs.

Lastly, it could be valuable to replicate human’s use of divergent thought in
the early stage of generating responses. As the response progresses, we could
gradually introduce additional constraints that restrict the generated output
to a more coherent and logical output, mirroring the convergent thought we see
in humans. This would contribute to the reduction of cognitive dissonance by
allowing high creativity, with the model maintaining logical consistency. One
way this could be implemented is by exploring different levels of decaying
temperature that initially allow these models to explore and then converge to
a concrete output.

It is important to note that some papers have already demonstrated mitigation
of hallucinations using methods that are similar to metacognitive processes.
While it is unclear if the author intended to mirror these processes in their
work, the similarity is undeniable. For example (Ji et al., 2023) utilizes a
self-reflection process to reduce the prevalence of hallucinations.
Additionally, (Varshney et al., 2023) uses a self-inquiry methodology to
decide when to search for verification and then correct itself. Both of these
approaches closely mirror metacognitive processes in humans, and both
demonstrate remarkable improvements. This shows how our psychology-informed
approach could provide a highly effective path forward for reducing
hallucinations.

It is crucial to emphasize that the goal is not to implement true
metacognition in AI, as its feasibility remains uncertain. Instead, we propose
drawing inspiration from metacognitive processes when developing LLMs,
believing this approach could lead to more accurate, reliable, and responsible
AI systems. Embracing the lessons learned from human metacognition may pave
the way for significant advancements in the field.

##  6 Conclusion

In this work, we reexamine the term hallucinations and propose a better
taxonomy rooted in human psychology. By doing so, we open pathways for
creating targeted solutions by leveraging insights from human psychology. We
propose strategies, such as enhanced source attribution, source monitoring,
reflective processing, and other forms of artificial metacognition to address
challenges in LLMs. While acknowledging the uncertainties surrounding the
feasibility of true metacognition in AI, we emphasize the value of drawing
inspiration from human cognitive processes for the development of more
accurate and responsible LLMs. We also acknowledge that metacognition is
likely not the solution to all of the problems with hallucinations in LLMs, we
believe it could help us make significant progress. Additionally, leveraging
other psychological phenomena and resolution methods could help solve other
issues in LLMs. It is our hope that future research focuses on psychology-
informed methods to solve some of our most difficult challenges with LLMs.

##  7 Impact Statement

Our goal with this work is to provide a path forward to improving large
language models through the mitigation of hallucinations. Improving these
model comes with several societal impacts, but none that are unique to our
work.

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# SUPERB: Speech processing Universal PERformance Benchmark

###### Abstract

Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However,
the speech processing community lacks a similar setup to systematically
explore the paradigm. To bridge this gap, we introduce Speech processing
Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark
the performance of a shared model across a wide range of speech processing
tasks with minimal architecture changes and labeled data. Among multiple
usages of the shared model, we especially focus on extracting the
representation learned from SSL for its preferable re-usability. We present a
simple framework to solve SUPERB tasks by learning task-specialized
lightweight prediction heads on top of the frozen shared model. Our results
demonstrate that the framework is promising as SSL representations show
competitive generalizability and accessibility across SUPERB tasks. We release
SUPERB as a challenge with a leaderboard111https://superbbenchmark.org: SUPERB
welcomes pretrained model submissions. The framework described in this paper
will be used in the constrained track, in which the pretrained models are
frozen, and the prediction heads for downstream tasks are the same for all
pretrained models. We will open an unconstrained track for submissions with
any approach, including finetuning pretrained models and other non-SSL
approaches in the future. and a benchmark
toolkit222https://github.com/s3prl/s3prl: All the materials are open-sourced
and reproducible in s3prl toolkit which supports to benchmark most existing
and customized pretrained models. to fuel the research in representation
learning and general speech processing.

Index Terms: Speech, Self-Supervised Learning, Representation Learning, Model
Generalization, Benchmark, Evaluation

##  1 Introduction

Starting from ELMo [1] and BERT [2] in NLP, the effectiveness of SSL is
evident in various domains [3, 4]. It is becoming a new principle to solve
problems by pretraining a shared model with self-supervision tasks on a large
amount of unlabeled data to encode general-purpose knowledge. The model can
then be specialized in various downstream tasks through concatenating
prediction layers and simple finetuning. This approach achieves SOTA
performance in many applications.

SSL is desirable for its outstanding performance as well as generalizability
and re-usability across tasks to democratize deep learning to various
application scenarios. Developing deep neural networks is expensive nowadays
in terms of data collection, modeling, computing power, and training time.
Repeating the same process for each specific use case is time- and cost-
prohibitive for both academic and industrial researchers. SSL can
significantly speed up and lower the entry barrier for model development, as
the pretrained model is powerful to encode generally applicable knowledge, and
only requires low resources to extract task-specific knowledge for different
use cases. Well-established benchmark, such as GLUE [5] in NLP and VISSL [6]
in CV, is essential to evaluate pretrained models’ generalizability and re-
usability across a wide range of tasks.

SSL has been explored in speech, including pretraining with generative loss
[7, 8, 9, 10], discriminative loss [11, 12, 13, 14], or multi-task [15, 16].
Researchers have investigated these SSL models’ capabilities on tasks
including phoneme classification [11, 7], speaker identification [7, 8],
speaker verification [17], emotion recognition [15], ASR [9, 12, 10, 16],
speech translation [7], spoken language understanding [18], voice conversion
[19] and TTS [20]. While these works showed promising results of SSL on
various speech processing tasks, unlike CV or NLP areas, they were
investigated with different datasets and experimental setups. Absence of a
shared benchmark makes it hard to compare and draw insights across the
techniques. Furthermore, existing works explored a limited number of tasks or
require heavyweight downstream training [9, 12, 14], blurring the
generalizability and re-usability of SSL models across tasks. Both factors
limit the impact of SSL on speech processing in research and industry.

We introduce Speech processing Universal PERformance Benchmark (SUPERB) to
address the problem. SUPERB aims to 360-degree examine models’ capability and
collects various tasks with limited labeled data from speech communities to
align with common research interests. There are existing benchmarks proposed
to evaluate representations extracted from SSL pretrained models [21, 22].
[21] focuses on representations’ quality without any downstream training, and
[22] excludes the content recognition tasks like ASR. Compared to existing
efforts, SUPERB targets at the direct usability of pretrained models on
various popular tasks through any usage333Finetuning pretrained models or
using them as representation extractors are two common usages.. As finetuning
pretrained models typically requires huge resources and hinders the re-
usability, in this paper, we focus on investigating a simple framework solving
all SUPERB tasks with a frozen, shared pretrained model, and lightweight
prediction heads finetuned for each task. Our results show that the framework
yields competitive performance compared to traditional supervised pipelines by
leveraging powerful SSL representations, and they outperform log mel
filterbank (FBANK), a widely used feature in all speech domains, by a large
margin. Both results demonstrate the possibility of developing powerful,
generalizable, and reusable pretrained models to democratize the advance in
speech processing. We invite researchers to participate and submit new results
to drive the research frontier together1.

##  2 Speech processing Universal PERformance Benchmark

We establish and release Speech processing Universal PERformance Benchmark
(SUPERB), aiming to offer the community a standard and comprehensive testbed
for evaluating the generalizability of pretrained models on various tasks
covering all aspects of speech. General speech processing can be categorized
into discriminative and generative tasks. The former discriminates from
continuous speech into discrete decisions like a match in query-by-example,
words in ASR, and classes in speaker identification; the latter generates
continuous speech from any input like TTS, voice conversion, and source
separation. We focus on the former for the initial release of SUPERB444SUPERB
is a long-term maintained and continuously developing project. More pretrained
models will be included in the leaderboard, and we plan to release generative
tasks as the second challenge, like voice conversion and source separation..
Tasks are designed with the following principles: (1) conventional evaluation
protocols from speech communities, (2) publicly available datasets for
everyone to participate, (3) limited labeled data to effectively benchmark the
generalizability of models. Ten tasks are presented here to investigate four
aspects of speech: content, speaker, semantics, and paralinguistics.

###  2.1 Content

Four tasks are collected from ASR and Spoken Term Detection communities. The
former aims to transcribe speech into text content; the latter is to detect
the spoken content with minimal effort even without transcribing.

Phoneme Recognition, PR transcribes an utterance into the smallest content
units. We include alignment modeling in the PR task to avoid the potential
inaccurate forced alignment. LibriSpeech [23] train-clean-100/dev-clean/test-
clean subsets are adopted in SUPERB for training/validation/testing. Phoneme
transcriptions are obtained from the LibriSpeech official g2p-model-5 and the
conversion script in Kaldi librispeech s5 recipe. The evaluation metric is
phone error rate (PER).

Automatic Speech Recognition, ASR transcribes utterances into words. While PR
analyzes the improvement in modeling phonetics, ASR reflects the significance
of the improvement in a real-world scenario. LibriSpeech train-clean-100/dev-
clean/test-clean subsets are used for training/validation/testing. The
evaluation metric is word error rate (WER).

Keyword Spotting, KS detects preregistered keywords by classifying utterances
into a predefined set of words. The task is usually performed on-device for
the fast response time. Thus, accuracy, model size, and inference time are all
crucial. We choose the widely used Speech Commands dataset v1.0 [24] for the
task. The dataset consists of ten classes of keywords, a class for silence,
and an unknown class to include the false positive. The evaluation metric is
accuracy (ACC).

Query by Example Spoken Term Detection, QbE detects a spoken term (query) in
an audio database (documents) by binary discriminating a given pair of query
and document into a match or not. The English subset in QUESST 2014 [25]
challenge is adopted since we focus on investigating English as the first
step. The evaluation metric is maximum term weighted value (MTWV) which
balances misses and false alarms.

###  2.2 Speaker

Three tasks are collected to analyze speaker modeling.

Speaker Identification, SID classifies each utterance for its speaker identity
as a multi-class classification, where speakers are in the same predefined set
for both training and testing. The widely used VoxCeleb1 [26] is adopted, and
the evaluation metric is accuracy (ACC).

Automatic Speaker Verification, ASV verifies whether the speakers of a pair of
utterances match as a binary classification, and speakers in the testing set
may not appear in the training set. Thus, ASV is more challenging than SID.
VoxCeleb1 [26] is used without VoxCeleb2 training data and noise augmentation.
The evaluation metric is equal error rate (EER).

Speaker Diarization, SD predicts who is speaking when for each timestamp, and
multiple speakers can speak simultaneously. The model has to encode rich
speaker characteristics for each frame and should be able to represent
mixtures of signals. LibriMix [27] is adopted where LibriSpeech train-
clean-100/dev-clean/test-clean are used to generate mixtures for
training/validation/testing. We focus on the two-speaker scenario as the first
step. The time-coded speaker labels were generated using alignments from Kaldi
LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).

###  2.3 Semantics

Two tasks are collected from Spoken Language Understanding (SLU) community.
While most works for these tasks are done in two stages: transcribing speech
into text and predicting semantics on transcribed text, we focus on inferring
high-level semantics directly from raw audio in an end-to-end fashion.

Intent Classification, IC classifies utterances into predefined classes to
determine the intent of speakers. We use the Fluent Speech Commands [28]
dataset, where each utterance is tagged with three intent labels: action,
object, and location. The evaluation metric is accuracy (ACC).

Slot Filling, SF predicts a sequence of semantic slot-types from an utterance,
like a slot-type FromLocation for a spoken word Taipei, which is known as a
slot-value. Both slot-types and slot-values are essential for an SLU system to
function [18]. The evaluation metrics thus include slot-type F1 score and
slot-value CER [29]. Audio SNIPS [18] is adopted, which synthesized multi-
speaker utterances for SNIPS [30]. Following the standard split in SNIPS, US-
accent speakers are further selected for training, and others are for
validation/testing.

###  2.4 Paralinguistics

Emotion Recognition, ER predicts an emotion class for each utterance. The most
widely used ER dataset IEMOCAP [31] is adopted, and we follow the conventional
evaluation protocol: we drop the unbalance emotion classes to leave the final
four classes (neutral, happy, sad, angry) with a similar amount of data points
and cross-validates on five folds of the standard splits. The evaluation
metric is accuracy (ACC).

##  3 Framework: Universal Representation

Our framework aims to explore how simple and general the solution can be.
Thus, we freeze the parameters of pretrained models across tasks and extract
fixed representations to be fed into each task-specialized prediction head
(small downstream model). Compared to previous setups in speech representation
learning [9, 12, 13], the framework puts an explicit constraint on downstream
models to be as lightweight as possible for all tasks, as their parameter size
and required training resources are also crucial for the framework to be
simple and re-usable in various use cases. With the above principles, the
pretrained model solving all SUPERB tasks in this framework would be a
universal representation encoder. In the following, we first describe the SSL
pretrained models leveraged and then introduce the downstream models and
training policies.

###  3.1 Self-supervised pretrained models

SSL models explored in this paper are summarized in Table 1 and categorized
into three learning approaches: generative modeling, discriminative modeling,
and multi-task learning.

Generative modeling has long been a prevailing approach to learn speech
representation [7, 8, 10]. Instances of generative modeling investigated here
include APC [7], VQ-APC [32], Mockingjay [8], TERA [9], NPC [33] and DeCoAR
2.0 [10]. APC adopts the language model-like pretraining scheme on a sequence
of acoustic features (FBANK) with unidirectional RNN and generates future
frames conditioning on past frames. VQ-APC further applies vector-quantization
(VQ) layers onto APC’s representation to make it compact and low bit-rate.
Mockingjay adopts the BERT-like pretraining on Transformer encoders by masking
the input acoustic features in time axis and re-generating the masked parts.
TERA extends Mockingjay to further mask the frequency bins. NPC improves the
inference speed upon APC by replacing RNN with CNN and changing the future
generation to masked reconstruction as Mockingjay. DeCoAR 2.0 improves upon
Mockingjay by inserting a VQ layer right before the final prediction like VQ-
APC, and is trained by larger input mask, larger batch size, and more
unlabeled data.

Discriminative modeling for SSL studied here include CPC [11, 34], wav2vec
[12], vq-wav2vec [13], wav2vec 2.0 [14] and HuBERT [35]. CPC discriminates the
correlated positive samples from negative samples with contrastive InfoNCE
loss, which maximizes the mutual information between raw data and
representations. Modified CPC [34] and wav2vec [12] proposed several
architecture changes to improve CPC. vq-wav2vec introduces a VQ module to
wav2vec. The module discretizes speech into a sequence of tokens after InfoNCE
pretraining. Tokens are used as pseudo-text to train a BERT as did in NLP for
contextualized representations. wav2vec 2.0 merges the pipeline of vq-wav2vec
into one end-to-end training scheme by applying time masking in the latent
space and replacing BERT’s token prediction with InfoNCE’s negative sampling
to handle the intractable normalization on continuous speech. Motivated by
DeepCluster [36], HuBERT [35] enables BERT’s token prediction via off-line
clustering on representations. The clustered labels at the masked locations
are then predicted.

Multi-task learning is applied in PASE+ [16], where lots of pretraining
objectives are adopted: waveform generation, prosody features regression,
contrastive InfoMax objectives, and more. Multiple contaminations are also
applied to input speech like reverberation and additive noise.

###  3.2 Downstream models and policies

We design our framework to keep the downstream models and their finetuning
simple, while ensuring the performance across pretrained models is comparable
and the best model in each task is competitive. Since the last-layer
representation is not always the best, the framework collects multiple hidden
states from the pretrained model and weighted-sum them as the final
representation. For a fair comparison, we also limit the space for downstream
hyper-parameters tuning555We search for the best learning rate across 1e-1 to
1e-7 in log-scale for each combination of SSL representation and the
downstream tasks. More details about the allowed hyper-parameters tuning will
be available as we announce the challenge, but there will not be many hyper-
parameters to keep tuning simple.. Downstream models and algorithms are
summarized in the following and will be released in detail as a part of the
challenge policy.

PR, KS, SID, IC, ER are simple tasks that are solvable with linear downstream
models. Hence, we use a frame-wise linear transformation for PR with CTC loss;
mean-pooling followed by a linear transformation with cross-entropy loss for
utterance-level tasks (KS, SID, IC, and ER). These five tasks also serve as
the direct indication of representations’ quality following the conventional
linear evaluation protocol.

For ASR, a vanilla 2-layer 1024-unit BLSTM is adopted and optimized by CTC
loss on characters. The trained model is decoded with LibriSpeech official
4-gram LM powered by KenLM [37] and flashlight [38] toolkit. We mostly follow
the system proposed by GTTS-EHU for QUESST at MediaEval 2014 [39] for QbE but
replace the conventional supervised phoneme posteriorgram (PPG) with SSL
representations. We run Dynamic Time Warping[40] on all hidden states
separately with standard distance functions and obtain a score for each query-
document pair. The best distance function / hidden state pair is reported.
Regarding SF, slot-type labels are represented as special tokens to wrap the
slot-values in transcriptions. SF is then re-formulated as an ASR problem. The
finetuning scheme is the same as in our ASR task, except for the pre-
processing to encode slot-types into transcriptions and post-processing to
decode slot-types and slot-values from hypotheses. As for ASV, we adopt the
well-known x-vector [41] as the downstream model and change Softmax loss to
AMSoftmax loss with the same hyper-parameters as [26]. The simple cosine-
similarity backend is used to produce pairwise matching scores. We employ the
end-to-end training scheme with permutation-invariant training (PIT) loss [42]
to SD, instead of using clustering-based methods. We leverage a single-layer
512-unit LSTM for the downstream model.

Table 1:  Details of investigated SSL representations. LibriSpeech and
LibriLight are denoted as LS and LL, respectively. For the pretraining
methods, we abbreviate ”vector quantization” as VQ, ”future” as F, ”masked” as
M, ”generation” as G, ”contrastive discrimination” as C, and ”token
prediction/classification” as P. Parameters for both pretraining and inference
are counted.

Method | Network | #Params | Stride | Input | Corpus | Pretraining | Official Github
---|---|---|---|---|---|---|---
FBANK | - | 0 | 10ms | waveform | - | - | -
PASE+ [16] | SincNet, 7-Conv, 1-QRNN | 7.83M | 10ms | waveform | LS 50 hr | multi-task | santi-pdp / pase
APC [7] | 3-GRU | 4.11M | 10ms | FBANK | LS 360 hr | F-G | iamyuanchung / APC
VQ-APC [32] | 3-GRU | 4.63M | 10ms | FBANK | LS 360 hr | F-G + VQ | iamyuanchung / VQ-APC
NPC [33] | 4-Conv, 4-Masked Conv | 19.38M | 10ms | FBANK | LS 360 hr | M-G + VQ | Alexander-H-Liu / NPC
Mockingjay [8] | 12-Trans | 85.12M | 10ms | FBANK | LS 360 hr | time M-G | s3prl / s3prl
TERA [9] | 3-Trans | 21.33M | 10ms | FBANK | LS 960 hr | time/freq M-G | s3prl / s3prl
DeCoAR 2.0 [10] | 12-Trans | 89.84M | 10ms | FBANK | LS 960 hr | time M-G + VQ | awslabs / speech-representations
modified CPC [34] | 5-Conv, 1-LSTM | 1.84M | 10ms | waveform | LL 60k hr | F-C | facebookresearch / CPC_audio
wav2vec [12] | 19-Conv | 32.54M | 10ms | waveform | LS 960 hr | F-C | pytorch / fairseq
vq-wav2vec [13] | 20-Conv | 34.15M | 10ms | waveform | LS 960 hr | F-C + VQ | pytorch / fairseq
wav2vec 2.0 Base [14] | 7-Conv 12-Trans | 95.04M | 20ms | waveform | LS 960 hr | M-C + VQ | pytorch / fairseq
wav2vec 2.0 Large [14] | 7-Conv 24-Trans | 317.38M | 20ms | waveform | LL 60k hr | M-C + VQ | pytorch / fairseq
HuBERT Base [35] | 7-Conv 12-Trans | 94.68M | 20ms | waveform | LS 960 hr | M-P + VQ | pytorch / fairseq
HuBERT Large [35] | 7-Conv 24-Trans | 316.61M | 20ms | waveform | LL 60k hr | M-P + VQ | pytorch / fairseq

Table 2: Evaluating various SSL representations on various downstream tasks.
The numbers are collected with public-available checkpoints or codes, and we
welcome researchers to re-submit the results to our online leaderboard.

| PR | KS | IC | SID | ER | ASR (WER) | QbE | SF | ASV | SD
---|---|---|---|---|---|---|---|---|---|---
PER $\downarrow$ |  Acc $\uparrow$ |  Acc $\uparrow$ |  Acc $\uparrow$ |  Acc $\uparrow$ |  w/o $\downarrow$ |  w/ LM $\downarrow$ |  MTWV $\uparrow$ |  F1 $\uparrow$ |  CER $\downarrow$ |  EER $\downarrow$ |  DER $\downarrow$
FBANK | 82.01 | 8.63 | 9.10 | 8.5E-4 | 35.39 | 23.18 | 15.21 | 0.0058 | 69.64 | 52.94 | 9.56 | 10.05
PASE+ [16] | 58.87 | 82.54 | 29.82 | 37.99 | 57.86 | 25.11 | 16.62 | 0.0072 | 62.14 | 60.17 | 11.61 | 8.68
APC [7] | 41.98 | 91.01 | 74.69 | 60.42 | 59.33 | 21.28 | 14.74 | 0.0310 | 70.46 | 50.89 | 8.56 | 10.53
VQ-APC [32] | 41.08 | 91.11 | 74.48 | 60.15 | 59.66 | 21.20 | 15.21 | 0.0251 | 68.53 | 52.91 | 8.72 | 10.45
NPC [33] | 43.81 | 88.96 | 69.44 | 55.92 | 59.08 | 20.20 | 13.91 | 0.0246 | 72.79 | 48.44 | 9.4 | 9.34
Mockingjay [8] | 70.19 | 83.67 | 34.33 | 32.29 | 50.28 | 22.82 | 15.48 | 6.6E-04 | 61.59 | 58.89 | 11.66 | 10.54
TERA [9] | 49.17 | 89.48 | 58.42 | 57.57 | 56.27 | 18.17 | 12.16 | 0.0013 | 67.50 | 54.17 | 15.89 | 9.96
DeCoAR 2.0 [10] | 14.93 | 94.48 | 90.80 | 74.42 | 62.47 | 13.02 | 9.07 | 0.0406 | 83.28 | 34.73 | 7.16 | 6.59
modified CPC [34] | 42.54 | 91.88 | 64.09 | 39.63 | 60.96 | 20.18 | 13.53 | 0.0326 | 71.19 | 49.91 | 12.86 | 10.38
wav2vec [12] | 31.58 | 95.59 | 84.92 | 56.56 | 59.79 | 15.86 | 11.00 | 0.0485 | 76.37 | 43.71 | 7.99 | 9.9
vq-wav2vec [13] | 33.48 | 93.38 | 85.68 | 38.80 | 58.24 | 17.71 | 12.80 | 0.0410 | 77.68 | 41.54 | 10.38 | 9.93
wav2vec 2.0 Base [14] | 5.74 | 96.23 | 92.35 | 75.18 | 63.43 | 6.43 | 4.79 | 0.0233 | 88.30 | 24.77 | 6.02 | 6.08
wav2vec 2.0 Large [14] | 4.75 | 96.66 | 95.28 | 86.14 | 65.64 | 3.75 | 3.10 | 0.0489 | 87.11 | 27.31 | 5.65 | 5.62
HuBERT Base [35] | 5.41 | 96.30 | 98.34 | 81.42 | 64.92 | 6.42 | 4.79 | 0.0736 | 88.53 | 25.20 | 5.11 | 5.88
HuBERT Large [35] | 3.53 | 95.29 | 98.76 | 90.33 | 67.62 | 3.62 | 2.94 | 0.0353 | 89.81 | 21.76 | 5.98 | 5.75

##  4 Experiment

To extract representations from pretrained models, we follow the official
release as summarized in Table 1 for model definitions, pretrained weights,
and extraction pipelines if not mentioning specifically. Some noteworthy
details are: (1) NPC repository is used to pretrain APC and VQ-APC as it is
more flexible666It is preferable for its on-the-fly FBANK extraction to enable
testing representations on more corpora. Its APC implementation is mostly the
same as the official but with CMVN on FBANK. Its VQ-APC is an improved version
as stated in the official repository.. (2) For vq-wav2vec, we do not propagate
through BERT since its BERT implementation limits the utterance length which
is not long enough for some tasks.

We present the results in Table 2. For the tasks using linear models, FBANK
cannot work on any task, while SSL representations all perform well to some
degree with different specializations. It is a surprise that wav2vec 2.0 and
HuBERT conquers PR and IC with just linear models and outperforms others by a
large margin. Their results on SID and ER are also highly competitive. FBANK
achieves competitive performance when allowing non-linear downstream models in
ASR, SF, ASV, and SD, and yields better performance than some SSL
representations. We also observe that the ranking on PR aligns with ASR
weakly, while a significant improvement on phonetics still transfers to ASR,
like wav2vec, wav2vec 2.0, and HuBERT. Furthermore, wav2vec 2.0 and HuBERT
demonstrate that it becomes much easier than before to train an ASR system by
leveraging powerful SSL representations. HuBERT ranks the top one in QbE with
MTWV 0.074. The prevailing feature for QbE is PPG which we implemented with
TIMIT due to the current focus on English, and the result of TIMIT PPG is
0.052 in MTWV, suggesting that HuBERT turns out to be a very competitive
representation for QbE. As for SF, we can also observe a significant
improvement from wav2vec 2.0 and HuBERT over all other representations. The
CER in SF is generally high compared to ASR as many slot-values are named
entities. The results in ASV and SD show that many SSL representations are
worse than FBANK when it comes to real-world speaker problems beyond SID,
while HuBERT improves upon popular FBANK from 9.56 to 5.10 without additional
VoxCeleb2 or augmentation. Although we find it non-trivial for SSL
representations to generalize to all SUPERB tasks, wav2vec 2.0 and HuBERT
achieve highly competitive performance with only lightweight prediction heads
trainable, compared to traditional supervised techniques. The experiment
results exhibit the efficacy of developing a more generalizable and re-usable
pretrained model.

##  5 Conclusion

We present SUPERB, a challenge to generally benchmark the capability of SSL
pretrained models on speech processing. We demonstrate a simple framework to
solve all SUPERB tasks which leverages a frozen, shared pretrained model and
achieves competitive performance with minimal architecture changes and
downstream finetuning. We have open-sourced the evaluation toolkit2 and will
release the detailed challenge policy on the leaderboard website1. We welcome
the community to participate and drive the research frontier.

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