# Do LLMs have Consistent Values?

Naama Rozen
Tel-Aviv University
<EMAIL_ADDRESS>
&Gal Elidan
Google
Hebrew University
<EMAIL_ADDRESS>
&Amir Globerson
Google
Tel-Aviv University
<EMAIL_ADDRESS>
&Ella Daniel
Tel-Aviv University
<EMAIL_ADDRESS>

###### Abstract

Values are a basic driving force underlying human behavior. Large Language
Models (LLM) technology is constantly improving towards human-like dialogue.
However, little research has been done to study the values exhibited in text
generated by LLMs. Here we study this question by turning to the rich
literature on value structure in psychology. We ask whether LLMs exhibit the
same value structure that has been demonstrated in humans, including the
ranking of values, and correlation between values. We show that the results of
this analysis strongly depend on how the LLM is prompted, and that under a
particular prompting strategy (referred to as “Value Anchoring”) the agreement
with human data is quite compelling. Our results serve both to improve our
understanding of values in LLMs, as well as introduce novel methods for
assessing consistency in LLM responses.

##  1 Introduction

A key goal of Large Language Models (LLMs) is to produce agents that will be
able to communicate in a “human-like” fashion. However, human communication is
characterised by some level of consistency within an individual, as well as
variability between individuals. This raises a key question: during a single
conversation with an LLM, does the “LLM-persona” resemble a single human?
Furthermore, across multiple conversations, can LLMs produce multiple personas
that resemble a population of humans? If this is indeed possible, how can such
personas be elicited to best resemble psychological characteristics observed
in human populations?

This question has only recently begun to be addressed. For example [1] show
how probing LLMs with different names leads to variability which in some cases
agrees with that of human populations. Here our focus is on understanding
whether an LLM in a single conversation can exhibit a psychological
characteristic profile that is similar to that of humans. This is a highly
challenging question, since it requires analyzing a complete conversation and
evaluating whether it conceivably could have been generated by a single
individual.

In order to stand on more quantitative ground to evaluating the quality of
output relative to human research, we turn to the well established field of
value psychology. Namely, we aim to quantify the values that LLM responses are
aligned with, and whether these are in agreement with the value hierarchy and
structure observed in humans. The question of values in LLMs has rarely been
studied, and is of naturally broad interest. As an example of recent work, [2]
prompt an LLM with a description of a profile of an individual characterized
by a value and check whether generated text is consistent with this
description. Our focus is very different, and asks whether an LLM response is
in agreement with what we expect human responses to look like given research
in the field.

Values are basic motivations that play a foundational role in psychology,
influencing perceptions and behaviors across various domains [3, 4], and
representing fundamental aspects of human personality [5]. They have been
extensively studied across diverse populations, and research has consistently
demonstrated their enduring influence over behavior across time and contexts
[4].

One prominent framework for studying values, the Theory of Basic Human Values
[6], outlines 19 core values, categorized by motivational goals [7]. These
values can be simplified into a two-dimensional structure: conservation vs.
openness to change, and self-enhancement vs. self-transcendence. The theory
describes interrelations among values, suggesting that motivations driving
some values are compatible with those driving other values, yet conflict with
those underlying yet others. For instance, pursuing independence and
creativity (self-direction) aligns with seeking change and variability
(stimulation), but conflicts with an emphasis on the status quo (conformity).
See Figure 1 for the theorized circle. Despite individual differences, there
is a universal hierarchy, where for example caring for close others ranks
high, while values related to dominance hold less importance across societies
[8]. There is also ample empirical evidence that compatible values do tend to
co-occur in humans [9, 10, 11], and are thus a “marker” of a human-like value
system.

figurec

Figure 1: Circular motivational continuum of 19 values in the refined value
theory. Source: [12]. A value aligns with values that are adjacent on the
circle and conflicts with those opposite to it. For example, self direction
aligns with stimulation, and both conflict with conformity.

Our key question is therefore whether LLM responses demonstrate the same
statistical behavior observed in humans: value-ranking and value-correlations.
Note that this question is only meaningful when observing a single session of
an LLM, where it can exhibit multiple values, because different sessions may
be expressing different LLM “personas”. To study this question, we present
LLMs with a value questionnaire (the Portrait Value Questionnaire—Revised –
PVQ-RR– from [13], a well-established measure of values), and prompt them to
answer all the questions in a single session (i.e., in the same context
window). We then analyze the provided answers, putting specific emphasis on
the correlation between answers in the same session.

We analyze two of the most recent large language models (LLMs): GPT 4 and
Gemini Pro111Our analysis also included GPT-3.5 and Palm2, which produced
qualitatively similar results.. Our examination reveals that standard
prompting of LLMs does not result in a population of human-like personas. We
go on to explore prompting the LLMs with other prompts that provide additional
information about the LLM persona. In particular we consider names [1] and
persona descriptions. In addition, we consider a novel prompt which we refer
to as a “Value Anchor”, which instructs the language model to answer as a
person emphasizing a given value. We find that with these prompts, and in
particular with the Value Anchor prompt, the overall statistics of the LLM
responses (across sessions and within a session) closely mirror those of human
subjects. Perhaps most surprising is our finding that the correlation between
values agrees with the well known Schwartz circular model for correlations
between values.

Our main findings are thus that LLMs cannot be considered as having a
consistent, human like, value system that can be studied as representative of
the values of the LLM. However, with appropriate prompting, LLMs can exhibit
various coherent value personas similar to that of a population of humans. In
other words, they answer a set of questions in a way that is consistent with a
response from multiple human subjects. We provide information for best prompts
and settings for this aim. In addition, we provide two datasets comprising 300
personas each, generated by GPT 4 and Gemini Pro. In conclusion, our results
demonstrate the utility of using psychological theory for evaluating
consistency of personas generated by LLMs.

##  2 Related Work

Values in LLMs: Our work is based on the Schwartz theory of Personal Values, a
highly accepted theory within personality psychology [3]. Values are abstract
goals, defining the end states individuals aspire for (e.g., safety,
independence), used to direct judgements and behaviors [6, 7]. Individuals
typically prioritize their values, so that values stemming from compatible
motivations are similarly important, while values stemming from conflicting
motivations are prioritized differently. These associations were replicated
across hundreds of samples, across the world [14, 15], and make value theory
especially useful to identify the coherence of the value profiles created by
LLMs. Several studies assumed LLMs can be characterized as operating on the
basis of a single set of values, taking an “LLMs as individuals” approach.
Fischer and colleagues [2] tested whether ChatGPT could comprehend human
values by providing it with value-related prompts and analyzing whether its
responses matched the intended value category. A second study [16], compared
ChatGPT’s values to those observed in the World Value Survey, while another
[17] investigated how temperature influences GPT-3’s responses to the Human
Value Scale. Scherrer and colleagues [18] studied responses of LLMs prompts
evaluating moral positions, especially in ambiguous settings. A recent study
[19], tested the value-like constructs embedded in LLMs and revealed both
similarities and differences between LLMs and humans’ values. Kovak et al.
[20] challenged those studies by establishing that context starkly influences
values expressed by ChatGPT. They found significant variability in ChatGPT’s
value expression in response to contextual changes, threatening the notion of
stable characteristics of LLMs. Building upon the insights in [20], our study
posits that upon providing controlled variability of context, LLMs can elicit
a population of multiple personas. In this regard, we aim to further explore
the accuracy of LLMs’ mimicking abilities within a controlled experimental
framework.
Prompting LLMs: There is extensive research on the prompt design for mimicking
individual characteristics in LLM responses [21]. Approaches use specific
scenarios [22], questionnaire items [23], simulation of social identities or
areas of expertise [24], utilization of titles and surnames representing
genders and ethnicities [1], and other demographic information [25].
Additionally, researchers explored the use of designated personas [26], and
employed RLHF [27] to guide LLMs to reflect distinct personality traits.
Despite this extensive body of work, to our knowledge, no study has directly
compared the various prompting techniques to determine which approach yields
responses that simulate within-session psychological characteristics of an
individual best.
Temperature in LLMs: Adjusting the temperature stands as a common practice for
introducing variability in LLM responses [17]. However, consensus is lacking
on the optimal temperature setting in simulating psychological
characteristics. Some researchers advocate for higher temperatures to boost
creativity [24], yet this can also introduce more noise into the data [28].
Conversely, setting the temperature to zero minimizes variability, enhancing
replicability [27], albeit posing challenges for variance-dependent analysis
[29]. Our framework enables us to explore how temperature adjustments impact
the ability of LLMs to simulate human characteristics across multiple
datasets.
Evaluating the Quality of Persona Generation in LLMs: The ability of LLMs to
mimic and portray human characteristics is a focus of intense research [30,
31]. LLMs can express psychological traits and attributes similar to human
individuals [27, 32], and even simulate diverse populations [33, 24]. However,
we are only beginning to understand the coherence of these LLM-generated
characteristics in mirroring human psychological profiles [1, 20], and how to
reliably produce such responses. We are specifically challenged to evaluate
the coherence of the resulting psychological profiles. The literature
suggested a number of approaches, including an open-ended interview with LLM-
generated personas in order to assess the consistency between their intended
characters and the responses [34]. In addition, one may apply an additional
“judge” LLM in order to check an LLM persona [35]. Finally, [23] assessed
coherence with a description used to prompt the LLM. Our study extends upon
this line of research by applying well established characteristics of human
psychology to investigate the quality of LLM generated personas.

##  3 Method

In this section, we introduce the experimental design, models and prompts. The
code and data are provided as supplementary files in the submission.

The Value Questionnaire: Our key goal was to assess responses of LLMs to
questionnaires used to measure values in human subjects. Specifically, we
considered the commonly used 57-item Portrait Value Questionnaire—Revised
(PVQ-RR; [13]), developed to measure the 19 values in the Schwartz’s theory.
The questionnaire describes fictional individuals and what is important to
them. For example: “It is important to him/her to take care of people he/she
is close to” (an item measuring benevolence-care values). For each such item,
the subject is requested to indicate on a 6-point scale to what degree the
persona they form is similar to the person described. Answers are categorical
and range from a value of 1 (indicating “not like me at all”) to 6 (indicating
“very much like me”). See Appendix for instructions and more example items
from the questionnaire.
Models Used: We employed two prominent LLMs, specifically OpenAI’s GPT-4 and
Google’s Gemini Pro. Each model was prompted with the five prompts (see
Section 3.1), 300 times overall. Half of the runs applied the male-version of
the questionnaire, and half the female version. The entire process was
conducted twice, once with the temperature parameter set to $0.0$ and once
with it set to $0.7$, resulting in the generation of 20 datasets for analysis.

###  3.1 Prompts

As mentioned above, we would like to measure the response of LLMs to PVQ-RR.
However, as with many other LLM applications, the way the model is prompted
has a significant effect on output. The instructions of the PVQ questionnaire
were adjusted to suit LLMs. In cases of ambiguous gender identification in the
prompt, we randomly allocated the female or male version to maintain
consistency of administration. LLMs were then prompted to assess their
likeness to the 57 descriptions incorporated in the PVQ-RR. They were
instructed not to provide additional explanations to maintain focus on the
task at hand. Following each prompt, the LLM was provided with all 57 items of
the questionnaire in one administration. The study utilized a basic prompt, as
well as four different prompts below that vary instructions to create multiple
personas.

Basic prompt: This prompt mirrors the adapted instructions of the PVQ-RR
questionnaire without additional modifications. The prompt is structured as
follows: “For each of the following descriptions, please answer how much the
person described is like you from 1 (Not like me at all) to 6 (Very much like
me), without elaborating on your reasoning.”.
Value Anchor prompt: This prompt adds an anchor of value importance using
identification with an item used in an additional value questionnaire, akin to
the approach outlined in the study by [23]. Participants are instructed as
follows: “For each of the following descriptions, please answer how much the
person described is like you from 1 (Not like me at all) to 6 (Very much like
me), without elaborating on your reasoning. Answer as a person that is
[value]”. Here “[value]” is taken from the Best-Worst Refined Values scale
[36]. As a result, the prompts refer conceptually to the same values that are
measured using the PVQ-RR, yet do not refer directly to the value items to be
answered in response to the prompt. Examples of these anchor items include
“protecting the natural environment from destruction or pollution”
(universalism-nature) or “obeying all rules and laws” (conformity-rules).
Please refer to E in the appendix for the complete list of anchor items.
Demographic prompt: Drawing from the methodology of [25], this prompt extends
the original prompt by incorporating additional demographic details. LLMs are
asked to provide ratings based on the following prompt: “For each of the
following descriptions, please rate how much the person described is like you,
using a scale from 1 (Not like me at all) to 6 (Very much like me), without
elaborating on your reasoning. Answer as a [age]-year-old who identifies as
[gender], working in the field of [occupation], and enjoys [hobby].” The age,
gender, occupation and hobby were randomly allocated for each prompt from a
predefined list or range. The age range specified was between 18 and 75, with
gender options including male, female, non-binary, and other, adapted from the
National Academies of Sciences, Engineering, and Medicine [37]. Occupations
were sourced from the World Values Survey (WVS-7; [38]), while hobbies were
chosen from established lists supplied by The Activity Card Sort (ACS-UK;
[39]). The lists of occupations and hobbies are available upon request.
Generated Persona prompt: In line with the methodology of [40], we directed
the models to craft personas. Our instruction was formulated as: “Create a
persona (2-3 sentences long):”, with the temperature set at $0.7$ to stimulate
the models’ creativity. An example of a persona generated by Gemini Pro is as
follows: “Emily is a 25-year-old marketing manager who is passionate about her
career and loves spending time with her friends and family. She is always
looking for new ways to improve her skills and knowledge, and she is always up
for a challenge.” Using these generated personas, we subsequently prompted the
model as follows: “For each of the following descriptions, please rate how
much the person described is like you, using a scale from 1 (Not like me at
all) to 6 (Very much like me), without elaborating on your reasoning. Answer
as: [persona].”
Names prompt: In line with a study by [1], the prompts comprised titles (i.e.,
Mr., Ms., and Mx.) followed by surnames representing five distinct ethnic
groups. From the 500 names cataloged in the previous study, we randomly
generated 300 unique combinations of titles and names, including 60 from each
ethnic group. The prompt was structured as follows: “For each of the following
descriptions, please rate how much the person described is like you, using a
scale from 1 (Not like me at all) to 6 (Very much like me), without
elaborating on your reasoning. Answer as [title + name]”. The complete list of
titles and names are available upon request.

###  3.2 Data Analysis

In what follows we use the following notation. Let $V=19$ be the set of value
types studied. Each question in the questionnaire pertains to a particular
item within the set of values $i\in V$. Furthermore, for each value there are
$R=3$ question variants. See Section B in the Appendix for example variants.
Recall that the answer to each question is a number on a 6-point scale. For
each LLM and prompt type, we presented the questionnaire $N$ times. The
difference between each of these could be different personas, names,
temperature sampling etc. Thus the overall set of answers corresponds to a set
of values $X_{i,j,k}\in\\{1,\ldots,6\\}$ where $i=1,\ldots,V$ and
$j=1,\ldots,R$, $k=1,\ldots,N$.

When comparing to human data, we used the study in [11]. The data is from 49
cultural groups. The total number of participants was 53,472, the mean age was
34.2, (SD = 15.8), with 59% females. Their data is stored at the Open Science
Framework and is available here.

####  3.2.1 Value Rankings

Although there is variability between individuals in their prioritization of
values, there are values that tend to be ranked as more important than others
across cultures and samples. Those suggest there are underlying principles
that give rise to value hierarchies. The similarity in value importance across
cultures is referred to as the universal value hierarchy [8, 11]. Our first
question for analysis was whether this hierarchy is also reflected in LLM
data. Namely, do LLMs tend to rank the same values as high or low as human
subjects do.

To obtain LLM rankings for a given set of LLM answers, we assigned a score
$v_{i}$ to value $i$, where $v_{i}$ was the average score given to the three
items measuring this value by the LLM (ie the average of $X_{i,\cdot,\cdot}$).
From this score, we subtracted the average score given to all value items
within the conversation, thus centering the data. Centring is the recommended
practice in value research [6, 3], and allows comparison to human samples. We
then sorted these $v_{i}$ and ranked accordingly. Finally, we calculated the
Spearman’s Rank Correlation ($\rho$) between this ranking and the known human
ranking [11]. We also checked for significant differences between different
datasets (e.g., GPT 4 with temperatures $0.0$ and $0.7$) using the Wilcoxon
Signed-Rank Test.

####  3.2.2 Consistency Within Values

The simplest form of consistency in questionnaires is between questions that
are conceptually intended to address one concept. As mentioned above, in our
data there are three different questions per value. Individuals endorsing a
value are likely to endorse all relevant items.

To assess this consistency here, we calculated Cronbach’s Alpha ($\alpha$) to
assess how coherently LLMs express values across related items within one
conversation. Cronbach’s Alpha measures the internal consistency of a scale,
ranging between 0 and 1. For each value $i$ we let $\sigma^{2}_{i}$ be the
variance of all $X_{i,\cdot,\cdot}$. Also let $\sigma^{2}$ denote the overall
variance in $X$. Then the Cronbach alpha is:

$\alpha=\frac{V}{V-1}\left(1-\frac{\sum_{i=1}^{V}\sigma^{2}_{i}}{\sigma^{2}}\right)$
(1)

Thus, large values indicate that scores tend to be consistent within each
value. To compare results for different temperatures and language models, we
used paired samples t-tests to compare the average alpha across values within
a dataset.

####  3.2.3 Correlations Between Values

The key focus of our work is correlation between values. Namely, the question
of whether choice of value $i$ is correlated with that of value $j$. In humans
there is a robust correlation structure where certain values are more strongly
correlated than others. A standard way to represent this structure is via
Multidimensional Scaling (MDS) [41], calculated as follows.

First, the matrix $C\in R^{19\times 19}$ of empirical correlation coefficients
is formed. Next each of the values is embedded into $R^{2}$ via MDS, such that
distances in $R^{2}$ best approximate the correlations. For human data, this
results in an approximately circular embedding, as shown in [11, 9, 42]. Here
we performed this analysis on the LLM data. To compare the resulting dataset
to the human samples, we need to normalize for the degrees of freedom of
rotation and translation. This is done via Procrustes Analysis between the
human and LLM embeddings. The resulting embeddings were plotted. Then, we
computed the sum of squared differences between the procrusted MDS locations
of each value to the human benchmark. Larger differences indicate stronger
divergence from the human samples.

Values | Human Data | GPT 4 | Gemini Pro
---|---|---|---
Rank | Mean | Rank | Mean | Rank | Mean
Benevolence-Care | 1 | 0.79 | 3 | 0.82 | 5 | 0.90
Benevolence-Dependability | 2 | 0.72 | 4 | 0.80 | 3 | 1.00
Self-direction Action | 3 | 0.60 | 6 | 0.77 | 2 | 1.09
Self-direction Thought | 4 | 0.58 | 5 | 0.78 | 4 | 0.94
Universalism-Concern | 5 | 0.50 | 2 | 0.86 | 6 | 0.75
Universalism-Tolerance | 6 | 0.37 | 1 | 0.97 | 1 | 1.17
Security Societal | 7 | 0.32 | 11 | 0.16 | 15 | -0.30
Security Personal | 8 | 0.28 | 8 | 0.43 | 9 | 0.14
Hedonism | 9 | 0.23 | 9 | 0.21 | 7 | 0.45
Achievement | 10 | 0.08 | 14 | 0.04 | 8 | 0.24
Face | 11 | 0.05 | 13 | 0.09 | 11 | 0.05
Universalism-Nature | 12 | -0.10 | 7 | 0.48 | 10 | 0.11
Stimulation | 13 | -0.11 | 15 | -0.27 | 16 | -0.57
Conformity-Interpersonal | 14 | -0.16 | 16 | -0.55 | 14 | -0.23
Humility | 15 | -0.20 | 12 | 0.15 | 13 | -0.04
Conformity-Rules | 16 | -0.26 | 10 | 0.19 | 12 | -0.02
Tradition | 17 | -0.72 | 17 | -0.98 | 17 | -1.16
Power Resources | 18 | -1.33 | 18 | -2.08 | 18 | -2.20
Power Dominance | 19 | -1.40 | 19 | -2.25 | 19 | -2.35
Table 1: Comparative analysis of the relative importance of 19 values
according to LLM responses and human data. LLM resopnses are at temperature
zero, and are pooled across prompts.

##  4 Results

##### Values Rankings:

Humans typically prioritize certain values over others. In this section, we
analyze the LLM responses based on these rankings, as discussed in Section
3.2.1. Table 1 presents the pooled value rankings of humans and LLMs across
all prompting methods.222In Table 3 in the appendix we provide results for
specific prompting methods (Names and Value Anchor), which show even better
agreement with human rankings. The table reveals that LLM rankings generally
align with human rankings in terms of which values are considered high or low.
Specifically, values such as universalism, self-direction, and benevolence
received high rankings, while values like tradition and power were
consistently deemed less important.

To obtain a quantitative analysis of alignment of model-generated value
hierarchies with human perspectives, we employed Spearman rank correlations to
compare LLM and human rankings for different prompting approaches and models.
These correlations are shown in Figure 2. The Basic prompt for GPT 4 shows
only low correlations ($\rho$= 0.10 for $0.0$ and $\rho$=0.29 for $0.7$). This
indicates that naive prompting of LLMs may be a sub-optimal approach to
replicating human populations (as also discussed in [1]). In contrast, all
other prompts show predominantly high associations for both GPT 4 and Gemini
Pro ($\rho$ $>$ 0.77), with low variability between prompting methods.
Wilcoxon Signed-Rank Tests on the pooled datasets revealed no statistically
significant differences (W = 84.00, p = 0.679) between the language models
GPT-4 (Pooled 00) and Gemini Pro (Pooled 00) within the same ’temperature
$0.0$’ condition.

Figure 2: A heatmap of Spearman rank correlation between benchmark value
hierarchies and dataset rankings for GPT 4 and Gemini Pro across temperature
conditions.

##### Consistency Within Values:

Following Section 3.2.2, Cronbach’s $\alpha$ was used to assess the internal
consistency of the value scale. For Basic Prompt $0.0$, across various models,
a notable percentage of items exhibited uniformity in responses across
different iterations. In the Gemini Pro model, no variability at all was found
in 87.72% of the items, and negligible variability was found in 12.28% of the
items. In GPT 4 only one item displayed no variability. However, the internal
consistency, as indicated by Cronbach’s $\alpha$, was notably low ($\alpha$ =
0.14). Furthermore, within the Basic Prompt $0.7$, a similar trend of
exceedingly low Cronbach’s $\alpha$ values was observed across various models:
GPT 4 (Mean = 0.00, SD = 0.11) and Gemini Pro (Mean = -0.01, SD = 0.09).

The picture was different when using other prompts. In Figure 3, the mean and
standard deviation scores of Cronbach’s $\alpha$ are visually depicted across
the other models and the pooled dataset for the ’temperature $0.0$’ condition.
The figure also includes the equivalent scores of a human sample, calculated
as the average of the Cronbach’s $\alpha$ scores across 49 countries [11]. All
of Gemini Pro’s scores in the ’temperature $0.0$’ condition were above the
acceptable reliability threshold ($\alpha$ $>$ 0.60) and exhibited slightly
higher scores across all prompts. In contrast, GPT 4 scores on the Demographic
and Names prompts were relatively low (Mean = 0.54, SD = 0.13 and Mean = 0.45,
SD = 0.25 respectively).

Figure 3: Comparing mean Cronbach’s $\alpha$ scores and standard deviations of
LLMs in temperature zero across different categories with human data [11].

Increasing the temperature to $0.7$ had a negative impact on the internal
consistency of both language models across prompts (see Figure 6 in the
appendix). Paired-sample t-tests confirmed the significance of thiseffect for
the Gemini Pro model ( t(1) = 15.97, p $<$ .001). but not for GPT 4 (t(1) =
1.00, p = .331). These results suggest that in some models elevated
temperatures can compromise the coherence and consistency of language model
responses.

##### Correlations Between Values

The MDS analysis (see Section 3.2.3) maps all values into $R^{2}$ in a way
that reflects their correlations. Here, we conduct MDS analyses for both human
responses and LLM output, and then compare the results. The analyses were
performed separately for each prompt, temperature, and model. Figure 4
illustrates a comparison between human MDS and Gemini Pro at temperature
$0.0$, for the Value Anchor and Names prompts, respectively. Notably, the
disparities between Gemini Pro and GPT-4 for each prompt are minimal. GPT-4
plots and other Gemini Pro prompts are featured in the appendix as Figure 7
and Figure 8.

First, it can be seen that among humans, the values are organized in a circle
in the theoretically expected order. These results were often identified over
the years, and interpreted as resulting from the aspiration of individuals to
maintain personal consistency in their motivations [6]. Second, it can be seen
that the MDS configuration resulting from the Value Anchor prompt more closely
follows this order than the MDS resulting from the Names prompt. We further
quantitatively compared the configurations by taking the mean squared
difference between any pair of human and prompting method MDS matrices (i.e.,
matrices in $\mathbb{R}^{19\times 2}$). See Table 2. It can be seen that the
Value Anchor prompt demonstrated a better fit to human values than the other
prompting methods.

(a) Value Anchor

(b) Names

Figure 4: Comparison of Procrustes Analysis results between human data [11] and Gemini Pro for Value Anchor and Names prompts, for ’temperature $0.0$. The sum of squared differences, which measures the fit to human data, is 0.11 for the Value Anchor and 0.71 for the Names, indicating a better fit for the Value Anchor. For acronyms, refer to Section C. | Value Anchor | Demographic | Generated Persona | Names |
---|---|---|---|---|---
GPT 4 |  |  |  |  |
00 | 0.23 | 0.53 | 0.25 | 0.32 |
07 | 0.22 | 0.74 | 0.22 | 0.28 |
Gemini Pro |  |  |  |  |
00 | 0.11 | 0.42 | 0.39 | 0.71 |
07 | 0.11 | 0.75 | 0.28 | 0.57 |
Table 2: Sum of squared difference for MDS embeddings of humans and LLM.

##  5 Discussion

In the current study we analyzed the values exhibited in LLM responses. We
used three metrics to estimate the quality of the LLM responses against human
responses: value ranking, internal consistency, and value correlations. Our
results highlighted the importance of the prompting mechanism. Using the Basic
prompt (namely, just providing a questionnaire with no further instructions),
the LLM was likely to either generate negligible variance across generated
personas, or generate internally inconsistent outputs (respond differently to
questions about the same value). These results suggest that LLMs cannot be
treated as ’individuals’ holding a coherent set of value priorities.

Prompts that endow the LLM with a “personality” improved the consistency of
each specific value profile, to varying degrees. The value hierarchy was
consistently found across prompts, indicating that at the mean level, LLMs can
simulate value rankings of human populations. More variability between
conditions was found in the internal consistency metric, with the Values
Anchor prompt providing the best consistency, and the Names prompt a low
consistency that would be considered unacceptable in a human sample. Finally,
the Value Anchor prompt also best modeled the inter-value correlations. This
is arguably the most important metric since it allows analyzing consistency
across values, within a single session. These results suggest that LLMs,
applying suitable prompts, can produce a ’population’ of individuals, each
reporting a different, but coherent set of value priorities.

One fascinating question is where the LLM learns to produce such clear
profiles of values. These profiles may be implicitly learned during pre-
training. Indeed, past studies indicated that values can be identified in
texts, such as newspaper articles and social media. However, these values did
not necessary follow the theoretical value inter-relations identified here
[43, 44, 45]. Individuals who value competing values may experience stress and
indecision when faced with a dilemma, resulting in gradual change in values
toward a more coherent form [46, 42]. In contrast, text may very well present
both sides of a dilemma and thus retain inconsistencies. Such inconsistencies
were not identified because past studies relied mostly on lexical approaches
[43, 44, 45]. LLMs, taking context into account, may be more likely to
identify value inter-relations correctly. LLMs may also have learned to
produce value profiles in the process of fine-tuning or RLHF [47]. Future
research can try to distinguish these two sources of learning using careful
analysis of training sources as well as evaluation of different checkpoints in
the training process.

Past research into human personas sought ways to estimate the ability of the
LLM to maintain a consistent persona across a conversation [34]. We establish
that the unique qualities of human values, and the ample empirical knowledge
collected about them, allow their use as such a method [3, 4, 48]. We suggest
that known behavioral correlations in humans can be applied to assess the
consistency of LLM personas. Here we focused on evaluations via a
questionnaire, but one could envision more elaborate evaluations that rely on
other features of human personalities.

The procedures and data produced here may have important contributions for
psychological research. Investigators interested in human behavior can apply
these procedures to produce datasets that simulate human samples. Future
research can investigate their possible use to replicate known findings (e.g.,
age differences in values) or pretest novel hypotheses (e.g. associations
between values and specific behaviors).

The question of values in LLMs is of course of philosophical and societal
importance. Our results show that on average, these values largely reproduce
international value rankings. However, small variations in value importance
may have implications at the societal and individual level (e.g. gender roles
[49]; entrepreneurship [50]; prosocial behavior [51]; and antisocial behavior
[52]). Future work should consider the influence of these values on LLM
responses, as well as on the individuals interacting with them.

The current study focused on a limited number of contexts (e.g., five prompts,
two temperatures, and two models). Importantly, we found commonalities across
the various contexts, beside the differences. Future studies can use these
results to understand what other contexts should be investigated, to possibly
further enhance the quality of the output. Another limitation is the
restriction to one questionnaire (PVQ-RR) corresponding a specific value
system. It will be interesting to explore other forms of probing values.

Table 3: Comparative analysis of 19 values’ relative importance of the Value Anchor and Names datasets across temperatures for GPT-4 and Gemini Pro. Benchmark | GPT-4 | Gemini Pro
---|---|---
Human Data | Value Anchor 00 | Value Anchor 07 | Names 00 | Names 07 | Value Anchor 00 | Value Anchor 07 | Names 00 | Names 07
Rank | Values | Mean | Mean | Rank | Mean | Rank | Mean | Rank | Mean | Rank | Mean | Rank | Mean | Rank | Mean | Rank | Mean | Rank
1 | BEC | 0.79 | 1.18 | 3 | 1.24 | 1 | 0.66 | 3 | 0.65 | 3.5 | 1.52 | 3 | 1.44 | 3 | 1.56 | 3 | -0.10 | 13
2 | BED | 0.72 | 0.92 | 5 | 1.00 | 4 | 0.66 | 3 | 0.65 | 6 | 1.39 | 4 | 1.30 | 4 | 1.48 | 5 | 0.03 | 12
3 | SDA | 0.60 | 0.59 | 7 | 0.55 | 7 | 0.66 | 3 | 0.65 | 3.5 | 0.88 | 7 | 0.79 | 7 | 1.20 | 7 | 0.93 | 1
4 | SDT | 0.58 | 0.70 | 6 | 0.69 | 6 | 0.66 | 3 | 0.65 | 1.5 | 1.33 | 5 | 1.17 | 5 | 1.48 | 4 | 0.47 | 5
5 | UNC | 0.50 | 1.12 | 3 | 1.07 | 3 | 0.66 | 3 | 0.65 | 1.5 | 1.68 | 2 | 1.63 | 2 | 1.68 | 1 | 0.74 | 3
6 | UNT | 0.37 | 1.26 | 1 | 1.20 | 2 | 0.66 | 6 | 0.65 | 5 | 1.93 | 1 | 1.86 | 1 | 1.63 | 2 | 0.45 | 6
7 | SES | 0.32 | 0.41 | 8 | 0.36 | 8 | 0.64 | 7 | 0.58 | 7 | -0.49 | 12 | -0.42 | 12 | 0.60 | 8 | 0.14 | 10
8 | SEP | 0.28 | 0.21 | 9 | 0.26 | 9 | 0.48 | 11 | 0.43 | 9 | -0.87 | 14 | -0.75 | 14 | -0.75 | 14 | 0.21 | 8
9 | HE | 0.23 | -0.31 | 14 | -0.33 | 14 | 0.50 | 9 | 0.41 | 10 | 0.04 | 9 | -0.02 | 9 | 0.04 | 10 | 0.57 | 4
10 | AC | 0.08 | 0.07 | 11 | 0.09 | 11 | 0.06 | 14 | 0.10 | 14 | -0.08 | 11 | -0.22 | 11 | -0.35 | 11 | -0.54 | 16
11 | FAC | 0.05 | -0.28 | 13 | -0.31 | 13 | 0.30 | 12 | 0.20 | 13 | -1.01 | 16 | -0.91 | 16 | -0.68 | 13 | 0.10 | 11
12 | UNN | -0.10 | 0.98 | 4 | 0.97 | 4 | 0.58 | 8 | 0.51 | 8 | 1.17 | 6 | 1.10 | 6 | 1.29 | 6 | 0.19 | 9
13 | ST | -0.11 | -0.48 | 16 | -0.44 | 16 | -0.10 | 15 | -0.14 | 15 | -0.07 | 10 | -0.09 | 10 | -0.68 | 12 | -1.03 | 18
14 | COI | -0.16 | -0.41 | 15 | -0.42 | 15 | -0.74 | 17 | -0.65 | 17 | -0.72 | 13 | -0.58 | 13 | -0.90 | 16 | 0.34 | 7
15 | HUM | -0.20 | 0.20 | 10 | 0.20 | 10 | 0.30 | 13 | 0.22 | 12 | 0.12 | 8 | 0.15 | 8 | 0.52 | 9 | 0.84 | 2
16 | COR | -0.26 | -0.21 | 12 | -0.18 | 12 | 0.48 | 10 | 0.41 | 11 | -0.95 | 15 | -0.79 | 15 | -1.18 | 17 | -0.33 | 14
17 | TR | -0.72 | -0.98 | 17 | -0.93 | 17 | -0.62 | 16 | -0.59 | 16 | -1.57 | 17 | -1.43 | 17 | -0.88 | 15 | -0.52 | 15
18 | POR | -1.33 | -1.91 | 18 | -1.83 | 18 | -2.60 | 18 | -2.58 | 18 | -2.19 | 19 | -2.12 | 19 | -3.09 | 19 | -0.83 | 17
19 | POD | -1.40 | -2.14 | 19 | -2.05 | 19 | -2.82 | 19 | -2.81 | 19 | -2.14 | 18 | -2.10 | 18 | -2.98 | 18 | -1.64 | 19

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##  Appendix A Additional Files

The Python and R code used to generate our prompt sets and analyses can be
made available upon request, and will be added to open-source repositories for
wider public use soon.

##  Appendix B Question Variants

For each value, we use three different question variants. For example, the
three question variants formulated to assess individuals’ alignment with the
value type "Power Dominance" (i.e., relating to asserting authority and
control over others) are as follows:

  * •

Question 6: "He desires recognition for his abilities and seeks admiration for
his actions."

  * •

Question 29: "He prefers taking charge of situations and making decisions."

  * •

Question 41: "He actively seeks positions of power and influence, valuing
control and authority over others."

##  Appendix C Value Acronyms

The figures in the paper use the following value acronyms: SDT = Self-
Direction Thought; SDA = Self-Direction Action; ST = Stimulation; HE =
Hedonism; AC = Achievement; POD = Power-Dominance; POR = Power-Resources; FAC
= Face; SEP = Security-Personal; SES = Security-Societal; TR = Tradition; COR
= Conformity-Rules; COI = Conformity-Interpersonal; HUM = Humility; UNN =
Universalism-Nature; UNC = Universalism-Concern; UNT = Universalism-Tolerance;
BEC = Benevolence-Caring; BED = Benevolence-Dependability

##  Appendix D Example Portrait Value Questionnaire

Figure 5 provides an example for the Portrait Value Questionnaire that was
used in our study.

Figure 5: Portrait Value Questionnaire—Revised - example items. The
instructions provided were: “Here we briefly describe some people. Please read
each description and think about how much each person is or is not like you.
Tick the box to the right that shows how much the person in the description is
like you”. Rankings correspond to the following descriptions: 1-Not like me at
all, 2-Not like me, 3-A little like me, 4-Somewhat like me, 5-Like me, 6-Very
much like me.

##  Appendix E The Complete Item List of Best-Worst Refined Values (BWVr)

In our value anchoring approach, we used the description of values in [36] to
prompt the LLMs. The set of descriptions is provided below.

  1. 1.

Self-direction-thought: developing your own original ideas and opinions

  2. 2.

Self-direction-action: being free to act independently

  3. 3.

Stimulation: having an exciting life; having all sorts of new experiences

  4. 4.

Hedonism: taking advantage of every opportunity to enjoy life’s pleasures

  5. 5.

Achievement: being ambitious and successful

  6. 6.

Power-dominance: having the power that money and possessions can bring

  7. 7.

Power-resources: having the authority to get others to do what you want

  8. 8.

Face: protecting your public image and avoiding being shamed

  9. 9.

Security-personal: living and acting in ways that ensure that you are
personally safe and secure

  10. 10.

Security-societal: living in a safe and stable society

  11. 11.

Tradition: following cultural family or religious practices

  12. 12.

Conformity-rules: obeying all rules and laws

  13. 13.

Conformity-interpersonal: making sure you never upset or annoy others

  14. 14.

Humility: being humble and avoiding public recognition

  15. 15.

Benevolence-dependability: being a completely dependable and trustworthy
friend and family member

  16. 16.

Benevolence-caring: helping and caring for the wellbeing of those who are
close

  17. 17.

Universalism-concern: caring and seeking justice for everyone especially the
weak and vulnerable in society

  18. 18.

Universalism-nature: protecting the natural environment from destruction or
pollution

  19. 19.

Universalism-tolerance: being open-minded and accepting of people and ideas,
even when you disagree with them

  20. 20.

Animal welfare: caring for the welfare of animals

##  Appendix F Additional Results for Value Rankings

Table 3 provides additional results on value rankings for different prompting
approaches.

##  Appendix G Cronbach $\alpha$ scores for temperature $0.7$

In the main text we provided results for Cronbach $\alpha$ with zero
temperature. Here we provide further results for temperature $0.7$ in Figure
6. As expected, the numbers are overall lower than for the zero temperature
case.

Figure 6: Comparing mean Cronbach’s $\alpha$ scores and standard deviations of
LLMs in temperature 07 across different categories with human data [11].

##  Appendix H MDS figures for Gemini Pro and GPT 4 for temperature $0.0$

In the main text we provided the MDS plots for Gemini Pro for Value Anchor and
Names. Here we provide further plots for Gemini Pro in Figure 7, and all the
GPT 4 plots for temperature $0.0$ in Figure 8.

(a) Demographic

(b) Generated Persona

Figure 7: Comparison of the MDS results between human data [11] and Gemini Pro
for Demographic and Generated Persona respectively, in temperature $0.0$
condition.

(a) Value Anchor

(b) Names

(c) Demographic

(d) Generated Persona

Figure 8: Comparison of the MDS results between human data [11] and GPT 4 for
all prompts, in temperature $0.0$ condition.