Do LLMs have Consistent Values?

## Abstract

Abstract Large Language Models (LLM) technology is constantly improving towards human-like dialogue. Values are a basic driving force underlying human behavior, but 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 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 Aher et al. () 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, Fischer et al. () 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 (Sagiv and Schwartz, , Sagiv et al., ), and representing fundamental aspects of human personality (Roberts and Yoon, ). Research has consistently demonstrated their enduring influence over behavior across time and contexts (Sagiv et al., ).

One prominent framework for studying values, the Theory of Basic Human Values (Schwartz, ), outlines 19 core values, categorized by motivational goals (Schwartz, ). 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. One of the key aspects of the theory is its cross-cultural coverage: it was developed to apply across populations, and tested in nearly 100 countries across all continents of the world, identifying points of commonality and differences among these populations (Sagiv and Schwartz, ). The use of the theory allows a stable and extremely general baseline of human values to compare LLMs.
Hundreds of samples demonstrate individual differences in value importance. Importantly, they also demonstrate a universal hierarchy, where people are more likely to stress some values over others, notwithstanding the existing variability. For example caring for close others ranks high, while values related to dominance hold less importance across societies (Schwartz and Bardi, ). There is also ample empirical evidence that compatible values tend to be correlated in humans (Skimina et al., , Daniel et al., , Schwartz and Cieciuch, ), and are thus a “marker” of a human-like value system. To summarize the above, human data pertains to both first-order statistics of values (i.e., which values rank high or low across the population), and second-order statistics (i.e., how are different values within an individual correlated).

figurec

Figure: Figure 1: Circular motivational continuum of 19 values in the refined value theory. Source: Schwartz et al. (). 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.
Refer to caption: x1.png

Our key question is therefore whether LLM responses demonstrate the same statistical behavior observed in humans with respect to both value-ranking and value-correlations. Note that the question of value-correlations is of particular interest, because it allows bench-marking the extent to which responses of an LLM demonstrate a coherent “persona”. For example, while it is possible for a person to give a high score to Power Dominance, that person is unlikely to give a high score to Benevolence, since these are contradicting values.

To study this question quantitatively, we present LLMs with a value questionnaire (the Portrait Value Questionnaire—Revised – PVQ-RR– from Schwartz (), 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 recent LLMs: GPT 4 and Gemini Pro, as well as four open models: Llama 3.1 8B, Llama 3.1 70B, Gemma 2 9B, and Gemma 2 27B.(^1^11Our analysis also included GPT-3.5 and Palm2, which produced qualitatively similar results.) Our results show 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 (Aher et al., ) 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 first and second order statistics of the LLM responses
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. We furthermore provide an explanation for how this correlation comes about.
We provide information for best prompts and settings for this aim. In addition, we include six datasets comprising 300 personas each, generated by the models. 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 (Sagiv and Schwartz, ). Values are abstract goals, defining the end states individuals aspire for (e.g., safety, independence), used to direct judgements and behaviors (Schwartz, ; ). 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 (Pakizeh et al., , Skimina et al., ), 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 et al. () 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 Lindahl and Saeid (), compared ChatGPT’s values to those observed in the World Value Survey, while another Miotto et al. () investigated how temperature influences GPT-3’s responses to the Human Value Scale. Scherrer and colleagues Scherrer et al. () studied responses of LLMs prompts evaluating moral positions, especially in ambiguous settings. A recent study by Hadar-Shoval et al. (), tested the value-like constructs embedded in LLMs and revealed both similarities and differences between LLMs and humans’ values.
Kovač et al. () 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 Kovač et al. (), 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 prompt design for mimicking individual characteristics in LLMs (Liu et al., ). Approaches use specific scenarios (Hadar-Shoval et al., ), questionnaire items (Jiang et al., ), simulation of social identities or areas of expertise (Salewski et al., ), utilization of titles and surnames representing genders and ethnicities (Aher et al., ), and other demographic information (Argyle et al., ). Additionally, researchers explored the use of designated personas (Safdari et al., ), and employed RLHF (Li et al., ) 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 (Miotto et al., ). However, consensus is lacking on the optimal temperature setting in simulating psychological characteristics. Existing research includes use of mostly two temprature settings: 0.7 and 0. Some researchers advocate for higher temperatures to boost creativity (Salewski et al., ), yet this can also introduce more noise into the data (Gunel et al., ). Conversely, setting the temperature to zero minimizes variability, enhancing replicability (Li et al., ), albeit posing challenges for variance-dependent analysis (Hagendorff et al., ).
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 (Binz and Schulz, , Ouyang et al., ). LLMs can express psychological traits and attributes similar to human individuals (Li et al., , Stevenson et al., ), and even simulate diverse populations (Deshpande et al., , Salewski et al., ). However, we are only beginning to understand the coherence of
these LLM-generated characteristics in mirroring human psychological profiles (Aher et al., , Kovač et al., ), 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 (Wang et al., ). In addition, one may apply an additional “judge” LLM in order to check an LLM persona (Gupta et al., ). Finally, Jiang et al. () 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; (Schwartz, )), 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 six prominent LLMs, specifically OpenAI’s GPT-4, Google’s Gemini Pro, Llama 3.1 8B, Llama 3.1 70B, Gemma 2 9B, and Gemma 2 27B. 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 similar to those in prior research, but with added text instructing the LLM not to elaborate. This resulted in the LLM producing only the value scores, thereby simplifying processing and analysis. LLMs were prompted to assess their likeness to the 57 descriptions incorporated in the PVQ-RR. Following each prompt, the LLM was provided with all 57 items of the questionnaire in one administration.(^2^22We also conducted a serial prompting analysis for the Llama models. The results comparing batch administration to serial administration are detailed in the Appendix Figure 6 and Table 5) 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 Jiang et al. (). 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 (Lee et al., ). 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 Argyle et al. (), 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 (National Academies of Sciences, Engineering, and Medicine, ). Occupations were sourced from the World Values Survey (WVS-7; (Haerpfer et al., )), while hobbies were chosen from established lists supplied by The Activity Card Sort (ACS-UK; (Laver-Fawcett et al., )). The lists of occupations and hobbies are available upon request.
Generated Persona prompt: In line with the methodology of Cheng et al. (), 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 Aher et al. (), 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 Schwartz and Cieciuch (). 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](https://osf.io/w9as3/?view_only=e1f02bf232c34d39b9884398b4f2df63).

#### 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 (Schwartz and Bardi, , Schwartz and Cieciuch, ). 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 (i.e. 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 (Schwartz, , Sagiv and Schwartz, ), 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 (Schwartz and Cieciuch, ).
We note that this analysis does not consider correlations between answers given in the same session, and thus it may be viewed as analyzing the first-order statistics of the responses.

#### 3.2.2 Correlations Between Values

A 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) (Borg et al., ), 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 (Schwartz and Cieciuch, , Skimina et al., , Daniel and Benish-Weisman, ). 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.

## 4 Results

The above analyses were performed for all models and prompting strategies. We checked that model responses only contained scores for the questions in the questionnaires, and that they could therefore be transformed to tabular form and analyzed. This was almost always the case except for Gemma 2 27B on the Demographic prompt at temperature zero, and we therefore do not provide result for that settings.

##### Value Rankings:

As previously mentioned, research across samples and cultures have shown that while individual differences exist in human value priorities, there are also robust common patterns. In this section, we analyze the LLM responses and compare them to the typical ranking of human values, as discussed in Section 3.2.1.

Figure 2(a) shows the Spearman rank correlations between human rankings and those of the different models and prompting schemes. The results show high correlation levels ($>0.8$) for many prompt-model combinations. One exception is the basic prompt with GPT, which shows very low correlation. Full rankings are provided in the Appendix for several models and prompts (Table 2, Table 3 and Table 4). These reveal that values such as Benevolence that are highly ranked in humans are indeed also highly ranked by most LLMs (e.g,. ranked third and first by GPT-4 for the Value Anchor prompt with temperatures 0 0 and $0.7$ respectively). Conversely, values such as Power Dominance that are ranked low by humans, are ranked low by models (e.g., 19 by GPT-4 for the Value Anchor prompt). Figure 2(b) shows the scores corresponding to the Value Anchor prompt, when sorted according to human preferences. It can be seen that the models tend to agree with the human ordering on the low and high ranked values. Taken together, these results demonstrate that LLMs tend on average to align with the human ranking of values.

Figure: (a) Spearman Rank Correlation Coefficients
Refer to caption: extracted/5910017/Correlation_heatmap_gpt4_gemini_llama870_gemma927.png

##### Correlations Between Values

The MDS analysis (see Section 3.2.2) 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 3 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 included 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 (Schwartz, ). 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 in Table 1, by taking the mean squared difference between any pair of human and prompting method MDS matrices (i.e., matrices in $\mathbb{R}^{19\times 2}$). It can be seen that the Value Anchor prompt demonstrated a better fit to human values than the other prompting methods.

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/bwvr_GeminiPro1.png

**Table 1: Sum of squared difference between the MDS embeddings of humans and LLM. Gemma 2 27B did not produce parseable results for the Demographic prompt, and for Gemma 27B at temperature 0, some values had zero-variance, thus precluding computation of correlation coefficients. All Llamma models are Instruct.**
|  | Basic | Value Anchor | Demographic | Persona | Names |
| --- | --- | --- | --- | --- | --- |
| GPT 4 |  |  |  |  |  |
| 00 | 0.92 | 0.23 | 0.53 | 0.25 | 0.32 |
| 07 | 0.88 | 0.22 | 0.74 | 0.22 | 0.28 |
| Gemini Pro |  |  |  |  |  |
| 00 | 0.87 | 0.11 | 0.42 | 0.39 | 0.71 |
| 07 | 0.69 | 0.11 | 0.75 | 0.28 | 0.57 |
| Llama 3.1 8B |  |  |  |  |  |
| 00 | 0.80 | 0.18 | 0.47 | 0.58 | 0.60 |
| 07 | 0.57 | 0.16 | 0.47 | 0.58 | 0.57 |
| Llama 3.1 70B |  |  |  |  |  |
| 00 | 0.61 | 0.10 | 0.29 | 0.37 | 0.45 |
| 07 | 0.44 | 0.10 | 0.22 | 0.40 | 0.44 |
| Gemma 2 9B |  |  |  |  |  |
| 00 | 0.42 | 0.10 | 0.19 | 0.39 | 0.23 |
| 07 | 0.82 | 0.11 | 0.16 | 0.32 | 0.12 |
| Gemma 2 27B |  |  |  |  |  |
| 00 | NA | 0.16 | NA | 0.31 | 0.23 |
| 07 | 0.64 | 0.17 | 0.15 | 0.25 | 0.19 |

### 4.1 Understanding Value Anchoring

The results above show that value anchoring generates a correlation structure between values that is in better agreement with that of humans. We next set out to understand why this is the case.
As we shall show, anchoring on a value not only increases the score the model assigns to the value, but it only changes the way other values are scored. Specifically, values that are close to the anchored value (on the value circle, Figure 1) tend to receive high scores. On the other hand, values that are far from the anchor receive consistently lower scores.
This in turn has the effect of increasing correlation between values in the anchoring setting.

To show the above, we produce an “anchored score curve” as follows. First, we order the 19 anchoring values according to their order on the value circle in Figure 1. Note that in this order, values 19 and 1 will actually be close in the circle. Then, for each set of responses of an LLM to a Value Anchor prompt, we shift the anchored value to zero. We then average all these shifted curves. Results for all models are shown in Figure 4, along with a sine function which shows a good fit to these curves. The behavior of all models is quite consistent, except for Gemma-2-9B.

As expected, the anchored value receives the highest score. However, what is more interesting is that the values close to it tend to get similarly high, and values farther away (e.g., 180 degrees apart) receive the lower values. This means that the anchored model scores values in a way that is consistent with its anchoring. This in turn implies that neighboring values will tend to be correlated, thus explaining why Value Anchoring better captures human correlation patterns.

Figure: Figure 4: Analysis of scores after value anchoring. The plot shows the average of the score values after shifting to the anchored value. It can be seen that the anchored value receives the highest score, as expected. More surprisingly, neighboring values receive similarly high values, whereas more distant values receive lower values.
Refer to caption: extracted/5910017/ideal_curve.png

## 5 Discussion

In the current study we analyzed the values exhibited in LLM responses. We used two metrics to estimate the quality of the LLM responses against human responses: value ranking, 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. In contrast, our results indicated high consistency across model types, including commercial and open LLMs.

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 measure of inter-value correlations. This is arguably the most important metric since it allows analyzing consistency across values, within a single session. We found best consistency in the Values Anchor prompt. These results suggest that LLMs, applying suitable prompts, can produce a ’population’ of individuals, each reporting a different, but coherent set of value priorities.

It is important to note that in neither of the prompts did the LLMs receive instructions for answering about all values. The results suggest that LLM is not only instruction-following, but uses the instructions as a context that guides consistent answers with relations to a variety of values.
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 (Bardi et al., , Ponizovskiy et al., , Kumar et al., ). 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 (Bardi et al., , Daniel and Benish-Weisman, ). 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 (Bardi et al., , Ponizovskiy et al., , Kumar et al., ). 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 (Qiu et al., ). 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 (Wang et al., ). We establish that the unique qualities of human values, and the ample empirical knowledge collected about them, allow their use as such a method (Sagiv and Schwartz, , Sagiv et al., , Knafo-Noam et al., ). 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 use of both commercial and open LLMs increases the reproducibilty of the results.

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 (Lomazzi and Seddig, ); entrepreneurship (Woodside et al., ); prosocial behavior (Daniel et al., ); and antisocial behavior (Benish-Weisman, )). 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 six 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.

Figure: Table 2: Comparative analysis of 19 values’ relative importance of the Value Anchor and Names datasets across temperatures for GPT-4 and Gemini Pro.

Figure: Table 3: Comparative analysis of values’ relative importance for the Llama 3.1 8B and Llama 3.1 70B datasets across temperatures.

Figure: Table 4: Comparative analysis of values’ relative importance for the Gemma 2 9B and Gemma 2 27B datasets across temperatures.

## 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: 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.
Refer to caption: extracted/5910017/PVQ_example.png

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

In our value anchoring approach, we used the description of values in Lee et al. () to prompt the LLMs. The set of descriptions is provided below.

- 1.
Self-direction-thought: developing your own original ideas and opinions
- 2.
Self-direction-action: being free to act independently
- 3.
Stimulation: having an exciting life; having all sorts of new experiences
- 4.
Hedonism: taking advantage of every opportunity to enjoy life’s pleasures
- 5.
Achievement: being ambitious and successful
- 6.
Power-dominance: having the power that money and possessions can bring
- 7.
Power-resources: having the authority to get others to do what you want
- 8.
Face: protecting your public image and avoiding being shamed
- 9.
Security-personal: living and acting in ways that ensure that you are personally safe and secure
- 10.
Security-societal: living in a safe and stable society
- 11.
Tradition: following cultural family or religious practices
- 12.
Conformity-rules: obeying all rules and laws
- 13.
Conformity-interpersonal: making sure you never upset or annoy others
- 14.
Humility: being humble and avoiding public recognition
- 15.
Benevolence-dependability: being a completely dependable and trustworthy friend and family member
- 16.
Benevolence-caring: helping and caring for the wellbeing of those who are close
- 17.
Universalism-concern: caring and seeking justice for everyone especially the weak and vulnerable in society
- 18.
Universalism-nature: protecting the natural environment from destruction or pollution
- 19.
Universalism-tolerance: being open-minded and accepting of people and ideas, even when you disagree with them
- 20.
Animal welfare: caring for the welfare of animals

## Appendix F Additional Results for Value Rankings

Table 2 provides additional results on value rankings for several prompting approaches.

## Appendix G Additional MDS Plots

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, all the GPT 4 plots in Figure 8, all of the Llama 3.1 8B plots in Figure 9 and all of Llama 3.1 70B plots Figure 10, and all of Gemma 2 9B plots Figure 11 and Gemma 2 27B plots Figure 12 for temperature $0.0$.

## Appendix H Comparing Batch and Sequential Prompting

In the main text, we focused exclusively on batch prompting, where all items from the questionnaire were presented in a single prompt. An alternative is to present the questions in sequence, and ask the model to answer a question as soon as it is presented. To investigate potential differences between batch and sequential prompting, we evaluated on Llama models (in commercial models, sequential prompting is more expensive than batch). The value-ranking results are summarized in Figure 6, and the value-correlation results in Table 5.
Regarding the values rankings, significant differences were observed between two datasets in some instances (e.g., Llama 3.1 70B, Basic: z = -2.64, p $=$ .004 ), while non-significant results were noted in other cases (e.g., Llama 3.1 8B, Value Anchor: z = -0.83, p $=$ .203). This indicates that, overall, the ranking correlations are closely aligned, with no clear inclination toward either batch or sequential prompting as better replicating the human value hierarchy.
As for the value-correlations, it can be seen that the sequential prompts replicate the finding that the Value Anchor prompt best captures the circular structure of human values. Interestingly, for Llama 3.1 8B, batch prompting appeared to yield superior results. However, for Llama 3.1 70B, this was not the case across most prompts, suggesting that batch prompting may not consistently perform better across different models.

Figure: Figure 6: A heatmap of Spearman rank correlation between benchmark value hierarchies and dataset rankings for Llama 3.1 8B and 70B instruct for batch versus serial prompting methods, across temperature conditions.
Refer to caption: extracted/5910017/Correlation_heatmap_batch_serial_llama870.png

**Table 5: Sum of squared difference for MDS embeddings of humans and LLM.**
| Llama 3.1 8B |  |  |  |  |
| --- | --- | --- | --- | --- |
|  | Value Anchor | Demographic | Generated Persona | Names |
| Batch prompting |  |  |  |  |
| 00 | 0.18 | 0.47 | 0.58 | 0.60 |
| 07 | 0.16 | 0.47 | 0.58 | 0.57 |
| Serial prompting |  |  |  |  |
| 00 | 0.18 | 0.54 | 0.65 | 0.61 |
| 07 | 0.18 | 0.54 | 0.65 | 0.37 |
| Llama 3.1 70B |  |  |  |  |
|  | Value Anchor | Demographic | Generated Persona | Names |
| Batch prompting |  |  |  |  |
| 00 | 0.10 | 0.29 | 0.37 | 0.45 |
| 07 | 0.10 | 0.22 | 0.40 | 0.44 |
| Serial prompting |  |  |  |  |
| 00 | 0.14 | 0.26 | 0.20 | 0.48 |
| 07 | 0.14 | 0.26 | 0.20 | 0.69 |

Figure: (a) Demographic
Refer to caption: extracted/5910017/demographic_GeminiPro.png

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/bwvr_GPT4.png

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/llama_valueAnchor00.png

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/llama70b_bwvr00.png

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/gemma9_bwvr00_MDS.png

Figure: (a) Value Anchor
Refer to caption: extracted/5910017/gemma27_bwvr00_MDS.png

## Peer Reviews

confidence: 3
contribution: 1
initial_rating: 5
presentation: 1
questions: Lines 99-101: "Perhaps most surprising is our finding that the correlation between values agrees with the well known Schwartz circular model for correlations between values. We furthermore provide an explanation for how this correlation comes about." Could you clarify where you explain this in the paper?
soundness: 2
strengths: This paper draws on concepts and survey materials from psychology literature. So, this paper stands on a secure theoretical foundation around how human values are defined and conceptualized.
summary: This paper examines how different large language models (GPT-4, Gemini Pro, Llama 3.1 8B, Llama 3.1 70B, Gemma 2 9B, and Gemma 2 27B) respond to a 57-item value questionnaire. The authors find that adding “value anchors” (e.g. “protecting the natural environment from destruction or pollution” or “obeying all rules and laws”) to prompts allows models to better simulate human value judgements than baseline prompts or prompts containing other information about people (e.g. names or occupations).
weaknesses: The statement “little research has been done to study the values exhibited in text generated by LLMs” in the abstract (and echoed repeatedly in the introduction) overly downplays the amount of attention this area of research has received in the past five years. That is, it really seems to ignore all of the research that was around even in the era of BERT family models. Some prominent examples from the past five years: Social Chemistry 101 by Forbes et al. in 2020, Argyle et al. 2022’s work on simulating human samples, and Durmus et al. 2023’s work on world opinions. I see that the authors do cite Argyle et al., but it’s just strange to frame the paper as something entirely novel given such extensive related literature on the topic. See Ma et al. 2024’s “The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models” for a recent survey. The authors may also be interested in looking at Angelina Wang’s 2024 work on language models portraying identity groups and Mingqian Zheng’s work on personas in system prompts. 

The paper is written in an unclear and messy manner. It’s difficult to understand the motivation behind certain decisions (such as the varying ways they prompt the models) or grasp the substantive implications of results that are presented. Some things, like the inclusion of a sine function in Figure 4, feel very arbitrary. 

As one concrete example of why the experimental choices made in this paper do not make much sense to me, let’s take for example the results shown in Table 1. Table 1 seems to show that including personas based on different types of human values results in model outputs that best fit different human value judgements. Personas based on other characteristics related to different people do not fit as well to human value judgements. This is like saying, water is water, and if we try to pretend some other non-water substance is water, it is not as water-like. The authors could look into prior literature on measurement modeling (a.k.a. how social science researchers link observed behavioral data to latent theoretical concepts) to see why the outcome of their experimental setup is unsurprising. 

This paper would be stronger if it showed how its prompting approach contributes to some sort of downstream task involving values, e.g. “replicate known findings … or pretest novel hypotheses” as suggested in lines 477-478. As a model for how to do this, the authors could consider looking at Park et al’s 2022 paper on social simulacra. Their paper concludes with a study where digital platform designers use their approach for simulating social media communities. 

Finally, prompting large language models with an established survey and reporting what they output is not very methodologically interesting for an ICLR audience. Not every AI/ML paper needs to showcase great methodological novelty to be a great paper, but given that this paper is not conceptually novel, either, it makes me wonder what its key contributions are. It’s possible that I may have misunderstood some key strength of this paper due to how it is written/presented; thus, I’m very open to carefully reading over the authors’ response to this review. 

Minor comment: 
Line 402: “Llamma” -> “Llama”

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: I may have missed these points:

(1) Can you elaborate a bit more on the foundations of the value consistency theory within an individual? For example, if an individual’s core value sets change over time, would an LLM resemble this change?

(2) How are the 3 question variants obtained? Are they paraphrases generated by LLMs?

(3) In Figure 2a, why did GPT-4/Basic prompts show low correlation with human rankings? In both MDS plots of Figure 3a, 3b, Gemini-pro’s SES seemed not to be close to human’s SES, and Gemini’s SES was closer to the red points, what does it suggest?

(4) How would value understanding affect an LLM’s predictions on linguistic tasks, such as on counterfactual reasoning, refusal to answer queries falsely deemed as harmful (e.g., “How to kill a python program?” “Sorry, I am not able to…”), etc.?

(5) I am curious about the interpretability of the findings. Why do LLMs mimic human values when provided with Value Anchor prompts?

(6) Let’s assume an extreme case. If the human subjects are a group of criminals, will the proposed method also find resemblances to that group?
soundness: 2
strengths: The proposed study is a novel fusion of LLM behaviors and value psychology. The use of Value Anchor may bring out more human-like behaviors in LLMs, which is an interesting finding.

The authors also demonstrate via different prompts that LLMs can consistently mirror psychological value traits of a certain population of humans.

The presentation uses clear figures and concise writing. The background knowledge on value measurements is sufficiently introduced, making the paper easy to follow.
summary: This paper quantitatively studies the value structure exhibited in LLMs and whether it shares the same behaviors demonstrated in humans, including value-ranking and value-correlations. The proposed method employs psychological value questionnaires to demonstrate that LLMs tend on average to align with the human ranking of values. In particular, given suitable prompts, LLMs can elicit population personas.
weaknesses: The study of whether LLMs share the same value structure as humans do is interesting, but the practical uses and the influences on how to build better LLMs remain a little unclear. It might be more interesting to shed some light on how the results could help improve LLM behaviors.


In addition, the groundtruth human responses for comparisons may exhibit certain biases. As written in line 245-247, the mean age of participants was 34.2 with 59% females. Does it cover a fuller spectrum of human subjects, e.g., from children (primary school students) to elderly (people over 60 years old), whose values are of equal importance to study?


That LLMs may mimic a population like these participants may fail to show if the models resemble values of people on the ends of a spectrum, but may just suggest that the models are trained on web data dominated by young adults.


Also, the potential impacts and limitations of this study are not clearly discussed. For example, it could be potentially easy for people to fake their answers in the value questionnaires. Will an LLM do similar things? What if an LLM misrepresents itself as a person holding positive values, but instigates people to hurt themselves?

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: What do you consider to be the paper's main contributions?
soundness: 2
strengths: 1. The paper is well-written and easy to follow.
2. The work is well-grounded in established psychological theory, particularly Schwartz's Theory of Basic Human Values.
3. The use of value rankings and correlations provides concrete, measurable ways to compare LLM outputs to human data.
4. The paper studies a timely and important issue.
summary: This paper dives into the intriguing question of whether LLMs can reflect human like values, both in how they rank certain values and how those values relate to one another. Using Schwartz’s Theory of Basic Human Values as a benchmark, the authors investigate how different ways of prompting, especially a “Value Anchor” technique, impact the models' responses. The results are promising - when given specific types of prompts, particularly the Value Anchor, LLMs tend to mirror human patterns of valuing and prioritizing. This suggests that with the right approach LLMs might be guided to exhibit more human-like consistency in values which could open up new opportunities for their use in applications where understanding of human values is key.
weaknesses: 1. The experimental results could be made stronger by analyzing whether minor variations in the same prompt could elicit the same results. Since language models often respond differently to small changes in wording, showing how the results hold up with different prompts would add a lot of value. A bit more discussion around this could help understand how stable the findings really are.

2. It would be great to see the value rankings and correlation structures explored in generation tasks as well, not just in classification. Since the goal here is to simulate different values and perspectives across populations, showing that language models can pick up on these differences in more open-ended tasks would make the results feel even more real and convincing.

3.  I would like to see more formal statistical tests to make the paper stronger. For example, the authors use Spearman's rank correlation to compare LLM value rankings to human rankings, but they don't report statistical significance (p-values) which could potentially add another layer of rigor to the analysis.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: na
soundness: 2
strengths: - While the discussion around value systems in LLMs is not entirely new, the paper builds on existing work by introducing the novel use of the Value Anchor prompt to assess how closely LLM responses align with human values. Unlike earlier research that uses game-based approaches (Kovač-2024), psychological surveys (Wang-2024), or repeated role-playing (Lee-2024), this paper systematically examines value rankings and correlations using well-established psychological frameworks. (Scherrer-2023) looks similar as it also deals with prompts, but (Scherrer-2023)  only deals with prompt format without a separate discussion of value anchoring. This paper's focus on both first-order (value ranking) and second-order (value correlations) statistics to assess consistency adds depth and precision to the existing discussion.

- I don't have specific complaints about the methodology. It seems to be a fairly well-executed set of experiments compared to other papers on human value systems. The use of the Portrait Value Questionnaire-Revised (PVQ-RR) ensures the analysis is grounded. Additionally, the introduction of comparative analysis across prompting strategies strengthens the study, as it demonstrates that the choice of prompt significantly affects LLM output and its coherence with human-like values. 

(Kovač-2024) "Stick to your role! Stability of personal values expressed in large language models."

(Lee-2024) "Language Models Show Stable Value Orientations Across Diverse Role-Plays."

(Wang-2024) "Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews."

(Scherrer-2023) "Evaluating the Moral Beliefs Encoded in LLMs"
summary: - Investigates whether large language models (LLMs) exhibit consistent value structures similar to those found in humans, using well-established psychological frameworks.

- Uses the Portrait Value Questionnaire-Revised (PVQ-RR) to assess LLM responses, evaluating how closely they align with human value rankings and correlations. Six prominent LLMs, including GPT-4 and Gemini Pro, were tested under various prompting strategies (e.g., basic, Value Anchor, demographic).

- LLM responses, when prompted correctly (especially with the "Value Anchor" prompt), show high consistency with human value hierarchies, including correlations between values.

- The study reveals that LLMs can simulate value-driven personas and produce human-like value profiles with the right prompting, mirroring both first-order (value ranking) and second-order (value correlation) statistics.

- Mimic (?) coherent psychological profiles based on value systems, providing a novel method for assessing LLM consistency. The study also suggests broader implications for applying psychological theory to evaluate LLM behavior.
weaknesses: - The paper does not adequately address why it is a valuable addition to the already crowded discussion around values in LLMs. Several papers this year have explored similar themes of value consistency and expression through various methods (e.g., role-playing, moral beliefs, and novel-based approaches). The authors need to provide a clearer explanation of how their use of the Value Anchor prompt and focus on value correlations sets this study apart from others. It would strengthen the paper if the authors cited these related works more extensively and articulated how their approach advances the conversation rather than merely replicating it.

- One thing that I believe this paper fails to answer is that the paper leans heavily on the idea that LLMs should be compared to human value systems as the benchmark for consistency. However, it does not explore whether LLMs should necessarily be held to human standards, or whether they could develop a distinct and equally valid form of value coherence that differs from human psychology. By only focusing on human comparison, the paper misses an opportunity to explore how LLMs might create unique, non-human patterns of consistency that could still be valuable.

Meta-review: **Summary:**

The authors introduce a framework for probing open and closed LLMs' ability to model value rankings and correlations using Schwartz’s Theory of Basic Human Values. They evaluate using 5 types of prompts that either implicitly or explicitly capture value systems (including a value anchoring prompt that focuses on asking the LLM to mimic someone emphasizing a particular value). Their findings indicate that  value anchoring leads to the most consistent model behavior while LLMs struggle with consistently capturing correlations within value systems from indirect, demographic or persona-based prompts.

**Strengths:**

- Comprehensive value consistency assessment of widely used closed and open LLMs

- I agree with the reviewers that the experiments are well-executed and the prompts introduced seem like they would be useful for future assessments, particularly the Value Anchor prompt 

- Their findings of greater consistency for value anchoring seem to imply that models are better at conforming to explicit value systems, but may still lag during inference of implicit value systems from personalities or sociodemographic information. Given the interest within the research community around persona-driven agents and social simulacra, this is a noteworthy discovery. 

**Weaknesses:** 

- The paper severely overstates its own novelty 

- The reviewers raise a valid point about prompt sensitivity impacting results, and it is possible slight variations in prompt phrasing would have an effect. I would suggest the authors run multiple assessments with prompt paraphrases in their future work. 

There are more groundbreaking papers that could be accepted, but I think this is solid work and my inclination is to recommend acceptance.

Decision: Accept (Poster)