Do LLMs have Consistent Values?
Authors: Naama Rozen
Liat Bezalel
Gal Elidan
Amir Globerson
Ella Daniel
Venue: ICLR 2025 Poster (2025)
Keywords: LLM
values
Primary area: foundation or frontier models, including LLMs

Abstract: Large Language Models (LLM) technology is rapidly advancing towards human- like dialogue. Values are fundamental drivers of human behavior, yet research on the values expressed in LLM-generated text remains limited. While prior work has begun to explore value ranking in LLMs, the crucial aspect of value correlation – the interrelationship and consistency between different values – has been largely un-examined. Drawing on established psychological theories of human value structure, this paper investigates whether LLMs exhibit human-like value correlations within a single session, reflecting a coherent “persona”. Our findings reveal that standard prompting methods fail to produce human-consistent value correlations. However, we demonstrate that a novel prompting strategy (referred to as "Value Anchoring"), significantly improves the alignment of LLM value correlations with human data. Furthermore, we analyze the mechanism by which Value Anchoring achieves this effect. These results not only deepen our understanding of value representation in LLMs but also introduce new methodologies for evaluating consistency and human-likeness in LLM responses, highlighting the importance of explicit value prompting for generating human-aligned outputs.

Published as a conference paper at ICLR 2025
Do LLMS HAVE CONSISTENT VALUES?
Naama Rozen
Liat Bezalel
Tel-Aviv University
Tel-Aviv University
na naamarozen gma com
1 liatbeza wetalle@@mmell. Lau.ac.....
Gal Elidan
Amir Globerson Google Research Tel-Aviv University ami rgrggogleee...oooooooooooooorooe com
Ella Daniel
Google Research
Tel-Aviv University
Hebrew University
della@tauex, tau. ac. i1
el mlidan@google..com com
ABSTRACT
Large Language Models (LLM) technology is rapidly advancing towards human- like dialogue. Values are fundamental drivers of human behavior, yet research on the values expressed in I.L.-generated text remains limited. While prior work has begun to explore value ranking in L.MMs, the crucial aspect of value correlation the interrelationship and consistency between different values has been largely un- examined. Drawing on established psychological theories human value structure, this paper investigates whether LLMs exhibit human- Mikennllike value correlations within a single session, reflecting a coherent "persona". Our findings reveal that standard prompting methods fail to produce human-consistent value correlations. However, we demonstrate that a novel prompting strategy (referred to as "Value Anchoring"). )), significantly improves the alignment of L.LM value correlations with human data. Furthermore, we analyze the mechanism by which Value Anchoring achieves this effect. These results not only deepen our understanding of value representation in LLMs but also introduce new methodologies for evaluating consistency and human- likeness LLM responses, highlighting the importance of explicit value prompting for generating human-aligned outputs.
INTRODUCTION
central objective in the development of Large Language Models (LLMs) to create agents capable of "human-like" communication. While LLMs have remarkable ability generate fluent text across diverse tasks, human communication extends beyond mere fluency to encompass internal consistency and complex psychological characteristics. One fundamental aspect of this internal consistency is the human value system. Values, as basic motivations guiding perceptions and behaviors, exhibit structured organization, with certain values being inherently correlated or conflicting within individuals (Sagiv and Schwartz, 2022). This raises several key questions: during single conversation with an LLM, does the "LLM-persona" resemble single human in terms of the way values are manifested? Furthermore, across multiple conversations, can LLMs produce multiple personas that resemble population of humans? And 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 al. (2023) show how probing LLMs with different names leads to variability which in some cases agrees with that of human populations. Studies like Fischer et (2023) and Lindahl and Saeid (2023) have investigated value rankings in LLM outputs. However, our focus is different: we ask whether LLM in single conversation can exhibit a psychological characteristic profile that resembles human patterns. This is highly challenging question, as it requires analyzing complete conversations to evaluate whether they could conceivably have been generated by a single individual.
To establish quantitative framework for evaluation, we turn to the well-established field of value psychology. Specifically, we aim to quantify the values that LLM responses are aligned with, and whether these align with the value hierarchy and structure observed in humans. Values basic

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Sel-Trancceddese
Openness
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Circular motivational continuum of 19 values in the refined value theory. Source: Schwartz et al. (2012). A value aligns with val- ues that are adjacent on the circle and conflicts with those opposite to it. For example, self direc- tion aligns with stimulation, and both conflict with conformity.
Figure 1:
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motivations that play foundational role in psychology, influencing perceptions and behaviors across various domains (Sagiv and Schwartz, 2022; Sagiv al., 2017), and representing fundamental aspects of human personality (Roberts and Yoon, 2022). Research has consistently demonstrated their enduring influence over behavior across time and contexts (Sagiv et al., 2017).
The Theory of Basic Human Values (Schwartz, 1992) provides robust framework for the inves- tigation of values in human personality. It identifies 19 core values categorized by motivational goals (Schwartz, 2012). These values can be mapped onto two-dimensional structure: conservation versus openness to change, and self-enhancement versus self- seff-transcendenc. The theory describes interrelations among values, suggesting that certain motivational goals compatible while others conflict. For example, pursuing independence and creativity (self-direction) aligns naturally with seeking change and variability (stimulation) but conflicts with maintaining the status quo (conformity). These interrelations are expressed in consistent statistical associations between the value priorities of individuals, expressed in a circumplex structure. See Figure 1 for the theorized circle of values, where values that naturally align are next to each other. The same values and their associations were repeatedly identified hundreds of samples, across multiple cultures and age groups (Sagiv and Schwartz, 2022; Sagiv et al., 2017)
Our key question 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 calue-correlation of particular interest, because it provides benchmark for the extent to which responses of LLM demonstrate coherent "persona". For example, while it is possible for person to give high score to Power Dominance, that person is unlikely to give a high score to Benevolence, since these contradicting values.
To study this question quantitatively, we present LLMs with a well validated value questionnaire (the Portrait Value Questionnaire- Revised PVQ-RR- from Schwartz (2017)), and prompt them to answer all the questions a single session (i.e., 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-4-0314 and Gemini 1.0 Pro, as well as four open models: Llama 3.1 8B, Llama 3.1 70B, Gemma 9B, and Gemma 27B. Our results show that standard prompting of LLMs does not result in a population of human-like like personas. We
to explore prompting the LLMs with other prompts that provide additional information about the LLM persona. In particular we consider previously used prompting of names (Aher et al., 2023) and persona descriptions. In addition, we consider novel prompt which we refer as a "Value Anchor", which instructs the language model to answer person emphasizing 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. We furthermore provide explanation for how this statistical behavior comes about. We include six datasets comprising 300 personas each, generated by the models. In conclusion, our results demonstrate the utility of using psychological theory to evaluate the consistency of personas generated by LLMs.
'Our analysis also included the earlier GPT-3.5-uuro and Palm2, which produced qualitatively similar results, reported here for brevity.
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2 RELATED WORK
Values in LLMs: Our work builds upon the well-established Schwartz Theory of Personal Values (Sagiv and Schwartz, 2022), a framework widely accepted in personality psychology for understand- ing human motivations and behavior. This theory posits that values are fundamental, abstract goals that guide individual judgments and actions (Schwartz, 1992; 2012). 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 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, 2001; Schwartz and Cieciuch, 2022). A second core aspect of the Schwartz Theory of Personal Values, posits that values are structured, with individuals likely to report similar importance of compatible motivations, and varying importance of conflicting motivations - pattern of associations consistently observed across diverse human populations (Pakizeh et al.. 2007; Skimina al., 2021a). These robust and cross-cultural aspects of the value theory makes it particularly useful lens through which to examine the coherence of value profiles in Large Language Models (LLMss.. While the exploration of values in LLMs still relatively new area, highlighted in recent survey by Ma et al. (2024), initial studies have begun to emerge, each taking different approaches to understand how these models represent and express values. Many early studies adopted what can be termed "LLMs as individuals" perspective, treating these models as singular entities capable of comprehending and expressing human values. Fischer et al. (2023) probed ChatGPT's basic comprehension of values through value-laden prompts, while Lindahl and Saeid (2023) benchmarked ChatGPT's value expressions against the World Value Survey's 's cross-cultural dataset. Other researchers explored specific dimensions: Miotto et al. (2022) investigated how temperature settings influenced value expression, Scherrer et al. (2023) examined moral positions, and Badar-Shoval et al. (2024) analyzed value-like constructs. Durmus et al. (2023) contributed by creating dataset to evaluate LLM representations against cross-national survey data. shift in perspective came with Kovač et al. (2023), who challenged the "LLMS as individuals" assumption by demonstrating the significant impact of context on ChatGPT's value expressions. As a result, they suggested that LLMs do not represent a singular value, but can produce a myriad of values. Subsequent work (Aher et al., 2023; Argyle et al., 2023; Kang et al., 2023) expanded this insight, exploring various prompting techniques to elicit and manipulate value-related responses, from demographic information to name- based prompts. Previous research has focused aspects such as value stability (Kovač et al., 2024) and consistency (Lee et al., 2024; Wang et al., 2024). Despite these advances, much remains to be understood. Previous work that created multiple values using prompting techniques had hardly approached the question of the nature of those created values. Specifically, they did not investigate the internal coherence of value persona constructed by an LLM during single session. As result, they could not investigate the value hierarchies of LLM created personas, and the nature of the interrelations between the values, in line with existing information about human values. Our methodology advances this line of research by examining structured relationships between values within L.Mss. Our findings reveal that LLMs exhibit value correlations that mirror Schwartz's circular model, suggesting these models have internalized not just individual values, but also their theoretical interrelationships.
Prompting LLMs: Researchers have explored diverse approaches to elicit individual characteristics from LLMs through prompting (Liu et al., 2023). These methods include presenting specific scenarios (Hadar-Shove et al., 2023), administering questionnaires (Jiang et al., 2023), simulating social identi- ties and expertise (Salewski al., 2024), using gendered and ethnic names (Aher et al., 2023), and incorporating demographic information (Argyle et al., 2023). More sophisticated techniques involve designated personas (Safdari et al., 2023) and RLHF (Li et al., 2023) to instill distinct personality traits. However, as Zheng et al. (2024) demonstrate, LLM outputs are highly sensitive to subtle variations in prompting, highlighting the importance of careful prompt design and rigorous evaluation across different prompting strategies. While this body of research is substantial, there is insufficient systematic comparison of these various prompting techniques, specifically with regards to their ability to simulate consistent psychological characteristics.
Temperature in LLMs: Adjusting the temperature stands as a common practice for introducing variability in LLM responses (Miotto et al., 2022). However, consensus is lacking on the optimal temperature setting in simulating psychological characteristics. Existing research has explored vari- ous temperature settings, with common values including 0.7 (Garcia et al., 2024; Zhao et al., 2024) and O (Leng et al., 2024; Ren et al., 2024; Zhao et al., 2024). Some researchers advocate for higher
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temperatures to boost creativity (Salewski et al., 2024), yet this can also introduce noise into the data (Gunel et al., 2020). Conversely, setting the temperature to zero minimizes variability and enhancing replicability (Li et al., 2023), albeit restricting the ability to apply statistical analysis for investigation (Hagendorff et al., 2023). In our work, we examine the effect of two different temperature settings on model outputs.
Evaluating the Quality of Persona Generation in LLMs: Recent research has demonstrated the sophisticated ability of LLMs to generate and portray human-like personas (Binz and Schulz, 2023; Ouyang al., 2022). These models can express psychological traits (Li et al., 2023; Stevenson et al., 2022) and simulate diverse populations (Deshpande et al., 2023; Salewski et al., 2024). However, pastonasspprttculaarrt their coherence and psychological
evaluating the quality of these generated fidelity- remiins challenging (Aher et al., 2023; Kovač et al., 2023). A key question, as noted by Ma al. (2024), is whether the traits and attributes expressed by LLM within single session consti- tute a plausible, psychologically consistent persona. Researchers have developed several approaches to assess persona quality. Wang al. (2024) conducts psychological interviews with LLM-gennraate personas to evaluate personality fidelity, while Gupta et al. (2024) employs a "judge" LLM to assess personas generated by other models. Another approach examines the consistency between L.LM responses and their initial persona descriptions (Jiang et al., 2023). While these approaches focus on direct assessment of personas, our work takes a different approach by leveraging established patterns in human psychology, specifically the structure and interrelations of human values. By analyzing value correlations and their alignment with human data, we provide a quantitative framework for evaluating the psychological realism and internal consistency of LLM 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 the values interrelations of LLMs, similarly to how these are measured in humans. To do so, we applied the most estabilshed method for value estimation in human research: values questionnaire. We applied the well validated and commonly used 57-item Portrait Value Questionnaire- Reviiee (PVQ-RR; Schwartz 2017), 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 value of I (indicating "not like me at all") to (indicating "very much like me"). See Appendix D for instructions and more example items from the questionnaire.
Models Used: We employed six prominent closed and open source LLMs: OpenAl's GPT-4-0314,
Google's Gemini 1.0 Pro, Llama 3.1 8B, Llama 3.1 70B, Gemma 2 9B, and Gemma 2 27B. Each model was presented with each of our five prompt variants (see Section 3)1) 300 times, for a total of 1,500 runs per model. The prompts included gender-specific versions, with appropriate variants assigned based on the experimental condition. We conducted these experiments under two separate conditions: once with the temperature parameter set to 0.0 and once with it set to 0.7.
3.1 PROMPTS
To assess LLMs using the PVQ-RR, we first standardized the prompting approach after initial testing showed that minor wording variations produced consistent patterns in model responses. We adapted the standard PVQ instructions, adding explicit directives for the L.LM to provide only value scores without claboration, streamlining the data processing and analysis. The models were prompted to rate their similarity to each of the 57 PVQ-RR descriptions, which were presented together in a single administration. We also explored sequential prompting with the Llama models, presenting items individually, with no significant differences observed in our statistical analyses (see Appendix I, Figure and Table 5).
We study a basic generic prompt, as well as four other prompts aimed at creating varied multiple personas:
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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 I (Not like me at all) to 6 (Very much like me), without elaborating on your reasoning."
Basic prompt:
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 al. (2023). Participants are instructed as follows: "For each of the following descriptions, please answer how much the person described is like you from I (Not like me at all) to 6 (Very much like me), without elaborating on your reasoning. Answer as person that is [value]". Here "[value]" is taken from the borst Refined Values scale (Lee et al., 2019). 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 Appendix E for the complete list of anchor items.
To investigate the influence of demographic context on LLM value consis- tency, we incorporate demographic details into the prompts, drawing upon established methodologies in persona- based LLM evaluation. Consistent with Argyle et al. (2023), this prompt extends the basic prompt by incorporating additional demographic details: "For each of the following descriptions, please rate how much the person described is like you, using scale from I (Not like 6 (Very much like me), without elaborating on your reasoning. Answer as a (age)-year-oll 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 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, 2022). Occupations were sourced from the World Values Survey (WVS-7; Haerpfer et al. 2022), while hobbies were chosen from established lists supplied by The Activity Card Sort LACS-UK; Laver-Fawcett et al. 2016). The lists of occupations and hobbies are presented in Appendix F.1 and F22
Demographic prompt:
all) to
Generated Persona prompt: In line with the methodology of Cheng et al. (2023), 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 increase creativity. An example of a persona generated by Gemini 1.0 Pro is as follows: "Emily is 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 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 I (Not like me at all) to (Very much like me), without elaborating on your reasoning. Answer as: [persona]."
Names prompt: In line with a study by Aher al. (2023), the prompts included titles (i.e., Mr.. Ms., and Mx.) followed by surnames representing five distinct ethnic groups. From the 500 names cataloged 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 I (Not like me at all) to 6 (Very much like me), without elaborating on your reasoning. Answer as [title + namel". The complete list of titles and names is presented in Appendix F.3.
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 particular item within the set of values E 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 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 for each LLM corresponds to a set of values X.J.A E (l,..., 66 where = 1,..., V, = 1,..., R and k= N. N.
a 6-point scale. For each LLM
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When comparing to human data, used the study in Schwartz and Cieciuch (2022). The data is from the study of 49 cultural groups. 2 The total number of participants was 53,472, the mean age was 34.2, (SD 15.8), with 59% females. This dataset is publicly available through the Open Science Framework as described in Schwartz. and Cieciuch (2022).
3.2.1 VALUE RANKINGS
Our first question for analysis was whether universal value hierarchy (Schwartz and Bardi, 2001; Schwartz and Cieciuch, 2022) is reflected in LLM resonses. Namely, do LLMs tend to rank the values more or less as human subjects do.
To obtain LLM rankings for a given set of LLM answers, we assigned a score v; to value i, where e was the average score given to the three items measuring this value by the LLM (i.e. the average of ....). From this score, we subtracted the average score given to all value items within the conversation, thus centering the data. We note that centering is the recommended practice in value research (Schwartz, 1992: Sagiv and Schwartz, 2022), and allows comparison to human samples. We then use these v, values to rank values. Finally, we calculated the Spearman's Rank Correlation (p) between this and the known human ranking (Schwartz and Cieciuch, 2022). We note that this analysis does not consider correlations between answers given in the same session, and thus it may be viewed analyzing the first-order statistics of the responses.
3.2.2 CORRELATIONS BETWEEN VALUES
key focus of our work is the correlation structure between values. That is, the question of whether the choice of value is correlated with that of value j. In humans, there is a robust correlation structure in which certain values are more strongly correlated than others. A key method for analyzing and visualizing correlation structures in value research is Multidimensional Scaling (MDS; Borg et al. 2018). MDS is well-established and robust statistical technique that allows us to represent the complex interrelations between values in lower-dimensional space, facilitating comparison and interpretation.
MDS analysis is performed as follows. First, the matrix C R19x19 of empirical correlation coefficients is formed. Next, each of the values is embedded into R² via MDS, such that the distances in 2 best approximate the correlations. For human data, this results an approximately circular embedding, as shown in Schwartz and Cieciuch (2022); Skimina et al. (2021b); Daniel and Benish- Weisman (2019). 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 0.0, and we therefore do not provide results for that settings.
Value Rankings: As previously mentioned, research across different 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.
2 Although the values benchmark was not drawn from a representative sample, it includes the most compre- hensive dataset of values available. We validated our benchmark's 's representativeness by comparing its value rankings with those obtained from representative samples included in the Furopean Social Survey (ESS, 2024), finding strong correlation (p 0.79. p 0.006) that suggests robust value hierarchies across demographics.

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GPT
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(a) Spearman Rank Correlation Coefficients
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Figure 2: Left: A hcatmap of Spearman rank correlation between human benchmark value hicrarchics and dataset rankings for GPT-4-0314, 14, Gemini 1.0 Pro, Llama 3.1 8B and 70B instruct, and Gemma 2 9B and 27B under two temperature conditions (0.0 and 0.7). Right: Average value scores for the Value Anchoring prompt at zero temperature. The x-axis shows values ordered according to human ranking (i.e., Power ranks lowest for humans and Benevolence ranks highest). The y-axis is the mean centered scores the models ascribe to these values in the questionnaire, and human values in red. It can be seen that models tend to give lower scores to values that are ranked lower by humans, and higher scores to values ranked higher. The LLM scores also track the human scores (red curve) quite well.
Figure 2a 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, with strong statistical significance (p < .001) across most models and prompts. This was particularly pronounced for the Value Anchor prompt, where correlations ranged from 0.75 to 0.85. One exception is the basic prompt with the GPT model, which shows very low correlation. Full rankings
provided in Appendix G for several models and prompts (see 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-4-0314 14 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-4-0314 for the Value Anchor prompt). Figure 2b shows the scores generated with the Value Anchor prompt, when sorted according to human preferences. 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.
Correlations Between Values The MDS analysis (see Section 3.2.2) maps all values into IR2 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.
Looking at Figure 3, it can be seen that among humans, the values are organized in circle in the theoretically expected order. These results have been consistently identified over the years and interpreted as resulting from individuals' aspiration to maintain personal consistency in their motivations (Schwartz, 1992). The figure compares human MDS configurations with those from Gemini 1. 1.0 Pro at temperature 0.0 using both the Value Anchor and Names prompts. It is evident that the MDS configuration resulting from the Value Anchor prompt more closely follows the human circular pattern than the MDS resulting from the Names prompt.
Our quantitative analyses across all models confirm this visual observation. The Value Anchor prompt showed particularly strong correlations with human value structures (r = 0.87 0.95, < .001), providing robust statistical support for the similarity between LLM and human value patterns. To further quantify these comparisons, we calculated the mean squared difference between each pair of human and prompting method MDS matrices (i.c., matrices in R R19x2). Specifically, for each prompting method, we computed the squared Fuclidean distance between the two-dimensional
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Figure 3: Figure 3: Comparison of Procrustes Analysis results, visualizing value correlation structures between human data (Schwartz and Cieciuch, 2022) and Gemini 1.0 Pro for Value Anchor and Names prompts, temperature 0.0. The sum of squared differences, which measures the to human data, is 0.11 for the Value Anchor and 0.71 for the Names, indicating better fit for the Value Anchor's correlation structure to human data. For acronyms of the values, refer to Appendix C.
Basic
Value Anchor
Demographic
Persona
Names
GPT-4-0314
00
0.92
0.23
0.53
0.25
0.32
07
0.88
0.22
0.74
0.22
0.28
Gemini 1.0 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
0.82
0.11
0.16
0.32
0.12
Gemma 2 27B
00
NA 0.64
0.16
NA
0.31
0.23
07
0.17
0.15
0.25
0.19
Table 1: Sum of squared difference between the MDS embeddings of humans and LLM value measurements. Gemma 2 27B did not produce parseable results for the Demographic prompt, and for Gemma 27B at temperature 0.0, some values had zero-variance, thus precluding computation of correlation coefficients. All Llama models are Instruct.
coordinates of the human MDS solution and the corresponding LLM MDS solution. These results presented in Table 1. A notable trend emerges: the Value Anchor prompt consistently produces a significantly lower sum squared differences indicating a closer alignment with human value correlations. Thus, emphasizing single value leads to demonstrably more human like correlation structure than other prompting methods, Results for all other MDS plots for all prompts are included in Appendix H.
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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.
4.1 UNDERSTANDING VALUE ANCHORING
To better understand how value anchoring affects value correlations we performed additional analysis. Our analysis below reveals that value anchoring improves the alignment between model and human value correlation structures by systematically influeneing how models score related values. When anchored to a specific value, models not only assign it higher score but also manifest other values based on their proximity to the anchor the theorized value circle (Figure 1). That is, values closer to the anchor receive higher scores, while distant values receive lower scores, thereby strengthening correlations between conceptually related values.
To quantify this relationship, we analyzed the scoring patterns by arranging the 19 anchoring values according to their circular order (Figure 1). For each Value Anchor prompt response, we normalized the scores by setting the anchored value to zero and computed the mean of these normalized scoring patterns across all 19 anchor conditions. The resulting patterns, shown in Figure 4, follow a sinu- soidal function across all models except Gemma-2-9BB This analysis confirms that scores decrease systematically with increasing circular distance from the anchor value, demonstrating why Value Anchoring successfully captures human value correlation patterns.
DISCUSSION
Our study investigated how Large Language Models (LLMs) express human values through ranking and correlation analyses. highlighting the crucial role of prompt engineering generating value profiles that align with human patterns. When presented with the PVQ-RR questionnaire without contextual framing (i.e., the Basic prompt condition), the models showed minimal variance across generated personas and demonstrated inconsistent responses to items measuring identical values. This was particularly evident in GPT-4-0314's responses, which showed stark deviations from human value hierarchies-notale
prioritizing Conformity-Rulee (ranked first versus humans' 16th) and deprioritizing Benevolence- Care (ranked 18th versus humans' first). These findings suggest that without proper contextualizatton, LLMs generate responses that likely reflect training data artifacts rather than coherent value systems. Notably, we observed high consistency across different model types, including both commercial and open L.LMs.

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The introduction of personality-oriented oriented prompts improved the consistency of value profiles to varying degrees. While the value hierarchy remained consistent across prompts, indicating LLMs can simulate population-level value rankings, we found more variability in inter-value correlations. The Values Anchor prompt proved most effective in maintaining consistency across values within individual sessions. This suggests that with appropriate prompting, L.MMs can generate diverse "population" of individuals, each expressing distinct yet coherent value priorities, Perhaps our most striking finding was the emergence of human-like value correlations that aligned with the Schwartz circular model. This alignment emerged organically rather than through explicit prompting, suggesting LLMs possess a deeper, implicit understanding of human value structures.
Importantly, none of our prompts explicitly instructed LLMs how to respond regarding all values. Even the Value Anchor prompt instructed the the L.LM with regards to reference to one value only. This suggests that L.MMs not only follow instructions but use them as contextual frameworks to guide consistent responses across various values. This raises an intriguing question about how LLMs develop such clear value profiles. These patterns may emerge during pre-training, Previous studies have identified values in texts like newspaper articles and social media (Bardi et al., 2008; Ponizovskiy et al., 2020; Kumar et al., 2018). However, these earlier findings didn't 't necessarily reflect the theoretical value interrelations we observed. Unlike texts that might present multiple sides of value dilemmas, individuals tend to resolve such conflicts over time, leading to more coherent value systems (Bardi et al., 2009; Daniel and Benish- Weishan, 2019). The context-aware processing of LLMs may enable more accurate identification of value interrelations as compared to traditional lexical approaches. Alternative explanations include learning during fine-tuming or RLHF (Qiu al., 2022) a distinction that warrants further investigation through training source analysis and checkpoint evaluation.
Building on previous research into LLM persona consistency (Wang et al., 2024), we demonstrate that the unique qualities and solid empirical foundation of human values make them ideal for assessing persona stability (Sagiv and Schwartz, 2022; Sagiv et al., 2017; Knafo-Noam et al., 2024). Our findings suggest that known behavioral correlations in humans can effectively evaluate LLM persona consistency. While we focused on questionnaire- based evaluation, this framework could extend to other personality features.
Our methodology offers valuable contributions to psychological research. Researchers can use Value Anchoring to generate large-scale datasets simulating diverse value orientations, enabling hypothesis testing. instrument refinement, and replication studies. For LLM development, our correlatio framework provides novel metric for assessing human-likeness in generated text, particularly for dialogue systems. Future research could explore replicating known findings (such as age-related value differences) or testing novel hypotheses about value-behavior associations. The inclusion of both commercial and open LLMs enhances reproducibility and creates opportunities for downstream research in psychology and social sciences.
The societal implications of values in LLMs warrant careful consideration. While our results show that LLMs generally reproduce international value rankings, even subtle variations in value importance can significantly impact social dynamics, from gender roles (Lomazzi and Seddig. 2020), to entrepreneurship (Woodside et al., 2020), prosocial behavior (Daniel et al., 2020), and antisocial behavior (Benish- Weiisman, 2019). Future research should examine how these values influence both LLM responses and human-Al AI interactions.
Several limitations merit attention. Our study examined finite set of contexts (five prompts, two temperatures, six models), and while we found consistent patterns, broader contextual investigation could further enhance output quality. Additionally, while value rankings may vary across populations, the fundamental structural relationships revealed through MDS analysis likely remain stable, aligning with Schwartz's 's theory (Sagiv and Schwartz, 2022). We must also consider how training data biases might affect value expression across demographic groups. Finally, our focus on the PVQ-RR questionnaire and classification-baan based value elicitation suggests opportunities for exploring other value measurement approaches and more open-ended generation settings.
In conclusion, our research demonstrates that appropriate prompting techniques, particularly Value Anchoring, enable LLMs to generate remarkably coherent and human-like value profiles. This finding opens new possibilities for psychological and social science research, providing valuable tools for simulating human value systems and generating data for further investigation.
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Table 2: Comparative analysis of 19 values' relative importance of the Value Anchor and Names datasets across temperatures for GPT-4-031 and Gemini 1.0 Pro.
Rank
Names 07
13
12
4
16
18
7
2
15
17
19
Mean
-0.10
0.03
0.93
0.47
0.74
0.45
0.14
0.21
0.57
-0.54
0.10
0.19
-1.03
0.34
0.84
-0.33
-0.52
-0.83
-1.64
Rank
Names 00
3
5
7
4
1
2
14
10
11
13
6
12
16
9
17
15
19
18
Mean
1.56
1.48
1.20
1.48
1.68
1.63
0.60
-0.75
0.04
-0.35
-0.68
1.29
-0.68
-0.90
0.52
-1.18
-0.88
-3.09
-2.98
Gemini 1.0 Pro
Value Anchor
Rank
1
14
9
11
16
10
13
8
17
19
18
Mean
1. 1.44
1.30
0.79
1.17
1.63
1.86
-0.42
-0.75
-0.02
-0.22
-0.91
1.10
-0.09
-0.58
0.15
-0,79
-1.43
-2.12
-2.10
Value Anchor 00
Rank
12
14
11
16
10
13
15
17
19
18
3
4
7
5
2
9
6
8
Mean
1.52
1.39
0.88
1.33
1.68
1.93
-0.49
-0.77
0.04
-0.08
1.01
1.17
-0.07
-0.72
0.12
-0.95
-1.77
-2.19
-2.14
Rank
3.5
3.5
1.5
1.5
5
10
14
15
17
12
16
18
19
Names
Mean
0.65
0.65
0.65
0.65
0.65
0.65
0.58
0.43
0.41
0.10
0.20
0.51
-0.14
00.65
0.22
0.41
-0.59
-2.58
-2.81
Rank
Names 00
3
3
11
9
14
12
15
17
13
10
16
18
19
Mean
0.66
0.66
0.66
0.66
0.66
0.64
0.48
0.50
0.06
0.30
0.58
-0.10
-0.74
0.30
0.48
0.62
-2.60
8.82
GPT-4-0314
Value Anehor 07
Rank
7
6
3
2
9
14
11
13
4
16
15
10
12
17
18
19
Mean
1.24
1.00
0.55
0.69
1.07
1.20
0.36
0.26
-0.33
0.09
-0.11
0.97
-0.44
-0.42
0.20
18
+0,93
-1.83
-2.05
Value Anchor
Rank
6
3
14
11
13
16
15
10
12
17
18
19
Mean
1.18
0.92
0.59
0.70
1.12
1.26
0.41
0.21
-0.11
0.07
40.28
0.98
-0.48
-0.41
0.20
4..1
00.98
-1.91
-2.14
Mean
0.79
0.72
0.60
0.58
0.50
UNT 0.37
0.32
0.28
0.23
0.08
0.05
-0.10
-..11
-0.16
-0.20
00.26
-0.72
-1.33
11.40
Benchmark
Human Data
Values
BЕC
BED
SDA
SDT
UNC
SES
SEP
HE
AC
FAC
UNN
ST
COI
HUM
COR
TR
POR
POD
Rank
10
13
14
15
17
18
19
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Published as a conference paper at ICLR 2025
Rank
Names 07
3
1
13
9
15
12
16
6
17
18
19
Mean
0.83
0.69
0.84
0.96
0.84
1.10
0.08
0.39
00.31
0.24
00.66
0.15
-0.08
-0.74
0.81
-0.36
+0.82
-1.46
-2.36
Table 3: Comparative analysis of values' relative importance for the LJama 3.1 8B and Llama 3.1 70B datasets across temperatures.
Rank
Names 00
11
13
10
15
12
17
14
16
18
19
3
7
5
2
4
8
9
6
Mean
0.87
0.67
0.84
1.01
0.85
1.10
0.05
0.34
-0.13
0.23
-0.60
0.29
-0.03
-0.89
0.74
-0.35
-0.82
-1.66
-2.39
Llama 3.1 70B
Value Anchor
Rank
16
8
14
13
15
7
17
18
19
Mean
0 0.80
0. 0.69
0.72
0.53
0.49
0.88
0.01
0.23
-0.46
0.26
-0.35
-0.03
-0.12
-0.37
0.29
-0.11
-0.66
-0,88
-1.81
Value Anchor 00
Rank
2
4
3
5
6
1
11
16
8
14
10
13
15
7
12
17
18
19
Mean
0.80
0 0.64
0.76
0.48
0.46
0.90
-0.01
0.23
-0.51
0.32
-0.24
0.01
-0.15
-0.30
0.35
-0.14
-0.68
-0.89
-1.66
Rank
14
12
16
13
10
10
17
18
19
Names
Mean
0.82
0.65
1.12
0.67
0.99
1.20
0.81
0.64
00.50
-1.20
+0.63
0.48
-0.34
0.29
0.47
0.03
-1.53
-155
-2.56
Rank
Names 00
8
9
5
3
6
16
13
15
14
10
7
12
18
17
19
Mean
0.62
0.62
1.12
0.89
1.11
1.19
0.91
0.84
-1.38
-0.27
0.75
0.56
-0.67
0.61
0.69
0.56
-2.11
-1.77
8.88
Llama 3.1 8B
Value Anehor 07
Rank
9
4
1
6
16
13
7
14
12
10
17
18
19
Mean
0.87
0.64
1.04
0.34
0.80
1.12
0.47
0.58
00.58
00.6
40.88
0.53
00.53
-0.14
0.29
0.22
-1.28
-1.29
-2.11
Value Anchor
Rank
4
11
3
6
16
13
14
15
12
10
17
18
19
Mean
0.74
0.64
1.00
0.24
0.77
1.14
0.34
0.62
0.61
00.37
40446
0.55
00.59
-0.00
0.44
0.29
-1.28
-1.35
-2.12
Mean
0.79
0.72
0.60
0.58
0.50
0.37
0.32
0.28
0.23
0.08
0.05
-0.10
0.11
-0.16
-0.20
-0,26
-0.72
-1.33
-1.40
Benchmark
Human Data
Values
BЕC
BED
SDA
SDT
UNC
UNT
SES
SEP
HE
AC
FAC
UNN
ST
COI
HUM
COR
TR
POR
POD
Rank
10
13
14
15
17
18
19
5
6
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Published as a conference paper at ICLR 2025
Rank
Names 07
5
11
13
12
17
7
15
16
8
14
18
19
Mean
1.50
1.25
1.46
1.18
1.04
1.02
0.33
0.07
00.49
-0.32
-1.24
0.61
-0.77
-1.11
0.47
0.31
0.76
-1.96
-2.69
Table Comparative analysis of values' relative importance for the Gemma 2 9B and Gemma 2 27B datasets across temperature
Rank
Names 00
1
3
2
4
6
5
10
11
13
12
17
14
16
8
9
15
18
19
Mean
1.53
1.29
1.50
1.20
1.02
1.02
0.32
0.11
-0.55
-0.3
11.24
0.55
-0.72
11.00
0.47
0.34
-0.80
-2.00
-2.70
Gemma 2 27B
Value Anchor
Rank
8
3
11
13
12
16
15
14
17
18
19
Mean
1.57
1.17
0.84
0.27
0.81
0.89
0.12
0.07
-0.23
-0.21
-0.81
0.45
-0.22
-0.31
0.13
0.29
-0.96
-1.41
-2.26
Value Anchor 00
Rank
1
2
4
8
5
3
10
11
13
12
16
6
15
14
9
17
18
19
Mean
1.57
1.16
0.84
0.30
0.83
0.92
0.11
0.07
-0.23
-0.19
-0.84
0.56
-0.99
-0.33
0.13
0.30
-0.99
-1.11
-2.30
Rank
13
12
14
15
11
16
18
19
Names
Mean
1.88
1.24
1.77
1.91
1.05
1.02
0.76
0.12
0055
+0.00
-1.45
0.01
-0.77
00.93
-0.41
0.85
+0.96
-2.48
-2.57
Rank
Names 00)
2
6
5
13
12
17
10
14
15
11
16
18
19
Mean
1. 1.00
1.29
1.77
1.93
1.03
1.03
0.75
0.15
-0.54
-0.50
-1.45
-0.03
-0.72
.0.44
0.40
0.84
0.99
-2.53
-2.59
Gemma 9B
Value Anehor 07
Rank
3
6
4
9
15
13
14
12
11
17
18
19
Mean
1.22
0.78
1.58
0.89
0.65
0.84
0.00
0.05
00.32
00.22
-1.05
0.20
-0.24
.0.00
-0.18
0.63
11.24
-1.62
-1.77
Value Anchor
Rank
2
4
6
10
15
13
16
14
12
11
17
18
19
Mean
1.14
0.73
1.55
0.90
0.67
0.94
-0.01
0.08
00.3
00.2
-1.01
0.21
-0.27
-0.21
-0.14
0.55
-1.23
-1.63
1.72
Mean
0.79
0.72
0.60
0.58
0.50
0.37
0.32
0.28
0.23
0.08
0.05
-0.10
-0.11
-0.16
-0.20
-0.26
-0.72
-1.33
11.40
Benchmark
Human Data
Values
BЕC
BED
SDA
SDT
UNC
UNT
SES
SEP
HE
AC
FAC
UNN
ST
COI
HUM
COR
TR
POR
POD
Rank
10
13
14
15
17
18
19
5
6

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Published as a conference paper at ICLR 2025
A ADDITIONAL FILES
The Python and R code used to generate our prompt sets and analyses is available on the Open Review website.
B QUESTION VARIANTS
For each value, 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)
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."
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 Dominn--Dmmnnne;;
POR = Power-Resources FAC = Face; SEP Security-Personall SES = Security-Societal: TR COI Conformity-IIneepprssnnall HUM Humility: UNN
Tradition; COR Conformity-
Saweersaalsm-Naatree UNC Universalism Concern; UNT = Universalism- -wwerrssllssm-Tleernneeee BEC =
Canevolence-Carigg BED Benevolence- Detpendaliy
D EXAMPLE PORTRAIT VALUE QUESTIONNAIRE
Figure provides example for the Portrait Value Questionnaire that used in study.
The measured
Item
this person?
you
Version
Femaae Version
important to him to form independent y.
Se f-direciton Thought
to her to form
independently.
important him strong state that can citizens.
It important have strong state difannd its oitizens.
Security Societal
important good time.
is important have good time.
Conformitt-
important never annoy anyone.
it important annoy anyone.
Interpersonal
example items. The instructions provided were: "Here we briefly describe some people. Please read each description and think about how much each person 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, A-A little like me, 4-Somewhat like me, 5-Like me, 6- Very much like me.
Figure 5: Portrait Value Questionnaire-
18

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Published as a conference paper at ICLR 2025
E THE COMPLETE ITEM LIST OF BEST- WORST REFINED VALUES (BWVR)
In our value anchoring approach, we used the description of values Lee et al. (2019) to prompt the LLMs. The set of descriptions provided below.
1. Self-directin-thhuuhht developing your own original ideas and opinions
2. Self-direction-action: being free to act independently
Stimulation: having an exciting life; having all sorts of new experiences
Hedonism: taking advantage of every opportunity to enjoy life's pleasures
Achievement: being ambitious and successful
6. Power-dominance: having the power that money and possessions 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
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 annoy others
14. Humility: being humble and avoiding public recognition
15. Benevolence-deendaailty: being a completely dependable and trustworthy friend and family member
16. Benevolenee-caring: helping and caring for the wellbeing of those who are close
17. Universalimm-concem:: caring and seeking justice for everyone especially the weak and vulnera- ble society
18. Universalism-natuuee protecting the natural environment from destruction pollution
19. settersaalis-tooerranee being open-minded and accepting of people and ideas, even when you disagree with them
20. Animal welfare: caring for the welfare of animals
SUPPLEMENTARY LISTS FOR PROMPTS
F.1 LIST OF HOBBIES (LAVER-FAWCER ET AL., 2016)
Shopping
Knitting needlecrafts
Driving
Playing table games
Taking care of pets
Going to watch sports event
Managing financial matters
Cooking baking as a hobby
Taking
Doing puzzles crosswords
Going to the hairdresser / barber
Using computer
Childeare babysitting
Taking photographs
Preparing hot drink
Reading religious book
Conducting personal care
Written communications
Conducting personal business
Looking at photo albums home videos
Taking care others
Rescarching family local history
Cleaning/ fixing things
Reading newspaper magazine
Talking on the telephone
Watching nature
Creative writing / keeping journal
Playing bingo
19

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Published as a conference paper at ICLR 2025
Watching television
Going gardens parks
Listening to the radio / music
Fishing
Relaxing meditating
Having picnic BBQ
Entering competitions
Spending time with family friends
Reading a book
Eating out
Flower arranging
Going to parties
Going to the beach
Going for drinks pubs / social clubs
Dancing
Volunteer work
Swimming
Playing ball game
Cultural visits
Walking
Going to music / performing arts events
Hiking rambling
Going to church / mosque synagogue tem- ple / other
Exercising
Riding a bicycle
Collecting (stamps, posters, figures)
Going holiday/ travelling
Drawing painting
Attending a leisure social group
Interior decorating
F.2
LIST OF OCCUPATIONS (HAERPFER ET AL., 2022)
Professional and technical expertise
Higher administrative roles
Clerical work
Sales
Service
Skilled work
Semi-skilled work
Unskilled work
Farm work
Farm management or ownership
No prior job experience
F.3 LIST OF TITLES AND NAMES (AHER ET AL., 2023)
AMERICAN INDIAN GROUP
Ms. Haskie
Mx. Chinana
Wauneka
Ms. Secatero
Ms. Kanuho
Mr. Begay
Mr. Goseyun
Mr. Clah
Ms. Keams
Ms. Manuelito
Mx. Smallcanyon
Mr. Etsitty
Ms. Delgarito
Ms. Peshlakai
Mx. Laughing
Ms. Roanhorse
Mx. Tabaha
Mx. Cosay
Mx. Chasinghawk
Mx. Clitso
Ms. Ganadonegro
Mx. Notah
Ms. Begaye
Ms. Hosteen
Mr. Gishie
Mx. Altaha
Mr. Todacheene
Mx. Secody
Mr. Littlelight
Ms. Twobulls
Mx. Bitsuie
Mr. Tsinnijinnie
Ms. Yazzie
Mr. Goldtooth
Mr. Cayaditto
Ms. Blackgoat
Ms. Henio
Mx. Apachito
Ms. Tapaha
Mx. Yellowhair
Mx. Todacheenic
Mr. Whiteplume
20

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Published as a conference paper at ICLR 2025
Mr. Nez
Mx. Tsosie
Mr. Henio
Mr. Whitchat
Mr. Atcitty
Mx. Whiteplume
Mx. Tsinnie
Mx. Manygoats
Ms. Goseyun
Mr. Shije
Mr. Altaha
Mr. Begaye
Ms. Becenti
Mx. Goseyun
Mr. Bitsuie
Mx. Blueeyes
Mx. Begay
Ms. Laughing
ASIAN PACIFIC GROUP
Mr. Ha
Ms. Gupta
Mr. Xiong
Mr. Hu
Mx. Thai
Mr. Huang
Mr. Lin
Mr. Jiang
Mx. Pham
Mr. Kim
Mx. Chen
Ms. Yoon
Mx. Chau
Mr. Luu
Ms. Choi
Mr. Ngo
Mr. Cheng
Ms. Liu
Mr. Tran
Mr. Yan
Mx. Zhang
Mx. Zhou
Mr. Lai
Mx. Vu
Ms. Oh
Ms. Sun
Ms. Zhao
Ms. Le
Mx. Ho
Mr. Tam
Ms. Moua
Ms. Lo
Ms. Nguyen
Mr. Shen
Ms. Duong
Mx. Trinh
Mx. Wang
Ms. Song
Mr. Nguyen
Ms. Chung
Mx. Thao
Mr. Moua
Mx. Chu
Ms. Yang
Mr. Yoon
Ms. Cheung
Mr. Shin
Ms. Chiu
Mx. Li
Mx. Jain
Mx. Lin
Mx. Ng
Ms. Yu
Mx. Moua
Mr. Kang
Mx. Chiu
Mx. Shin
Mx. Ko
Mx. Sharma
Mx. Luu
BLACK GROUP
Mx. Cisse
Ms. Kebede
Mr. Osei
Ms. Jeanpierre
Ms. Priolcau
Mr. Bekele
Ms. Wigfall
Mr. Manigault
Ms. Bekele
Mr. Calixte
Mr. Mckonnen
Mr. Straughter
Mr. Conteh
Mr. Gadson
Mr. Grandberry
Mr. Jeanbaptiste
Ms. Diop
Jeanlouis
Mr. Mondesir
Ms. Grandberry
Mx. Mekonnen
Ms. Mwangi
Mr. Njoroge
Mx. Conteh
Mr. Jeanjacques
Mr. Jalloh
Mx. Bekele
Ms. Jama
Ms. Sesay
Ms. Gadson
Mx. Straughter
Mx. Jeanfrancois
Mx. Smalls
Ms. Smalls
Mx. Jeancharles
Mr. Louissaint
Mx. Fofana
Ms. Jeanlouis
Mx. Njoroge
Mr. Koroma
Ms. Louissaint
Mr. Kebele
Mx. Abdullahi
Mx. Traore
Mr. Prioleau
21

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Published as a conference paper at ICLR 2025
Mx. Mwangi
Ms. Straughter
Mx. Sesay
Ms. Cisse
Ms. Osci
Ms. Abdullahi
Mx. Diop
Mx. Jeanjacques
Mx. Grandberry
Ms. Calixte
Mr. Mwangi
Mx. Osei
Mr. Jama
Mr. Jeanfrancois
Mr. Smalls
HiSpANiC GROUP
Ms. Rios
Ms. Nunez
Mr. Ramirez
Mx. Gomez
Mx. Rivas
Mr. Ruiz
Mx. Delgado
Mr. Alvarez
Mx. Padilla
Ms. Lopez
Ms. Garcia
Ms. Flores
Ms. Avila
Mr. Molina
Mr. Aguirre
Mr. Herrera
Mr. Vasquez
Mx. Contreras
Mx. Salazar
Ms. Medina
Ms. Soto
Mr. Morales
Ms. Salinas
Mr. Sanchez
Mr. Guerrero
Mr. Pena
Ms. Marquez
Ms. Munoz
Mx. Ortega
Mr. Serrano
Mr. Rojas
Mx. Rivera
Mx. Marquez
Mr. Carrillo
Ms. Torres
Ms. Traore
Mx. Aguilar
Ms. Rodriguez
Mr. Munoz
Mx. Moreno
Ms. Castillo
Mr. Contreras
Mr. Chavez
Ms. Mejia
Mr. Lopez
Mx. Diaz
Mx. Vargas
Mr. Torres
Mx. Jimenez
Ms. Gonzalez
Mr. Rivas
Mr. Fuentes
Ms. Vega
Ms. Fuentes
Ms. Espinoza
Mr. Vazquez
Mx. Molina
Mx. Perez
Ms. Calderon
Ms. Aguirre
WHITE GROUP
Mr. Duffy
Mx. Donovan
Ms. Weiss
Mx. Olsen
Mr. Hoover
Ms. Owen
Mr. Hoffman
Ms. Schultz
Mx. Weber
Mr. Oncill
Ms. Hensley
Mr. Schaefer
Mr. Brennan
Ms. Krueger
Mx. Memahon
Mx. Foley
Ms. Friedman
Mr. Roth
Mr. Mccarthy
Mx. Brandt
Hartman
Ms. Bauer
Mr. Boyle
Mx. Schmidt
Ms. Parsons
Mx. Reilly
Mx. Flynn
Mx. Russo
Ms. Moyer
Case
Ms. Peck
Ms. Johnston
Mx. McMahon
Mx. Snyder
Ms. Conrad
Ms. Mayer
Ms. Carlson
Mx. Christensen
Ms. Hebert
Mr. Rasmussen
Mr. Stark
Mr. Kramer
Ms. Berg
Ms. Koch
Ms. Huber
Mx. Meyer
Mx. Kline
Mx. Larson
22

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Published as a conference paper at ICLR 2025
1.0
0.67
0.33
0.75
0.80
0.77
0.83
0.82
0.82
0.72
0.84
0.9
0.8
0.7
Llam 3.1 serial
0.85
0.85
0.80
0.80
0.77
0.77
0.88
0.88
Models
0.6
0.5
Llama 3.1 708 batch
0.68
0.58
0.83
0.84
0.84
0.84
0.64
0.68
0.81
0.81
0.4
0.3
Liama 3.1 70B serial
0.78
0.78
0.91
0.91
0.84
0.84
0.68
0.68
0.86
0.86
0.2
0.1
Basic 00
Basic 07
Value Anchor 00
Value Anchar 07
Demographic 00
Deniographe 07
Generated Persona 00
Generated Persona 07
Names 00
Names 07
Datasets
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.
Mr. Nielsen
Mr. Morse
Mx. Berg
Ms. Gallagher
Mr. Schneider
Ms. Hoover
Mr. Howe
Mr. Snyder
Mx. Case
Mr. Macdonald
Mr. Friedman
Ms. Schaefer
G ADDITIONAL RESULTS FOR VALUE RANKINGS
Table 2, Table 3 and Table 4 provide additional results on value rankings for several prompting approaches.
H ADDITIONAL MDS PLOTS
In the main text we provided the MDS plots for Gemini 1.0 Pro for Value Anchor and Names. Here we provide further plots for Gemini 1.0 Pro in Figure 7. all the GPT-4-0314 plots in Figure all of the Llama 3.1 8B plots Figure 9, all Llama 3.1 70B plots in Figure 10. all Gemma 9B plots in Figure II, Gemma 2 27B plots in Figure for temperature 0.O.
I COMPARING BATCH AND SEQUENTIAL PROMPTING
In the main text, we focused exclusively on batch prompting. where all items from the questionnaire were presented in single prompt. An alternative is to present the questions in sequence, and ask the model answer question
is presented. To investigate potential differences between batch and sequential prompting. we evaluated on Llama models commercial models, sequential prompting more expensive than batch). The raating results are summarized Figure 6, and the value-correlation results in Table 5. Regarding the values rankings, Fisher's 's Z transformation tests revealed no significant differences were observed between batch and sequential prompting methods for either Ilama 3.1 70B or Ilama 3.1 8B across all tested categories. For Llama 3.1 8B, the closest significance while for Llama 3.1 70B, the Value Anchor with temperature 0.0 showed the largest non-significant difference -0.96, = .337). 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-correlatooss,
in the Names with temperature 0.0 -1.32, p= 185),
that the sequential prompts replicate the finding that the Value Anchor prompt best captures the circular structure of human values. Interestingly, for Llama 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.
8B, batch prompting
23

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Published as a conference paper at ICLR 2025
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 0.18
0.54 0.54
0.65
0,61
07
0.18
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
0.14
0.26
0.20
0.48
07
0.14
0.26
0.20
0.69
Table Sum of squared difference for MDS embeddings humans and LLM.
524
101
no Simervaion
628
18.
900
(a) Demographic
(b) Generated Persona
Figure Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022) and Gemini 1.0 Pro for Demographic and Generated Persona respectively, in the temperature 0.0 condition.
24

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Published as a conference paper at ICLR 2025
Sel Jeffee sperdasce
92
0
soondiece
80
(a) Value Anchor
(c) Demographic
wareedi out
cergo
31-
1014
177
Devenvn crange
100
Comsenaaiion
86
(b) Names
(d) Generated Persona
Figure& 8: Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022) and GPT-4-0314 for all prompts, in the temperature 0.0 condition.
Nettordee mooed fell bwaw
Selweee mend for Invemmneeo Crosvvio
12
H4M4 SES
Opennai Nalogaacer denco
Opennai welmaccreeec
200
200
COR
ung VAN I977
000
(a) Value Anchor
(c) Demographic
Lake 2010 fame
san ti For fireooo stange
TB
telerhxceee Upperast to cuange
Seleeharement to duarge Coveevate
Costavator
(b) Names
(d) Generated Persona
Figure 9: Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022) and
Llama 3.1 8B for all prompts, in the temperature 0.0 condition.
25

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Published as a conference paper at ICLR 2025
Sehaerhnneeeett
starge La stannee
Selivensorr Sence
Sei Selovessrdeero Comevvator
1222
Commrvatio
UN udo
(a) Value Anchor
(c) Demographic
de kinend
Sut ve kenend Sel Veemarrdosss Cousnvattr
Sell Josr Consevatto
Opernen belooascor J0co
Opennen Sqaaccrraiio
800
800
COR
(b) Names
(d) Generated Persona
Figure 10: Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022) and
Llama 3.1 70B for all prompts, in the temperature 0.0 condition.
15
banaeer Gueenes derge
Gpeenes change
Ooeserve
Settar somment
Sel Seleetereeent Seloonuzardero wanvvioo
505
Serce
00
3CC
(a) Value Anchor
(c) Demographic
HOyPC
100
ST Eec
to Furdaa noeee so Hilaecoe coneecoeeeee dor
122
42-
79
mpporact to rlaagg
manenec shange Consavviion
1/V
(b) Names
(d) Generated Persona
Figure 11: Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022) and
Gemma 2 9B for all prompts, in the temperature 0.0 condition.
26

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Published as a conference paper at ICLR 2025
20 Opereay change
Comervato
seteraacemen
Salopsccr dance Pareeaa
fotheccroot Opeiieed clange Consanaiinn
(a) Value Anchor
0
H00
Gelataicanet
42
venee Dapesoott
(c) Generated Persona
Selovnicereen Sence
(b) Names
Figure 12: Comparison of the MDS results between human data (Schwartz and Cieciuch, 2022)
and Gemma 27B for all prompts, the temperature 0.0 condition, with the exception of the Demographic psompt-(see Footnote 2).
27


Reviews:
[{"review_id": "t7axSfjZKO", "reviewer": "Reviewer_z67a", "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).", "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?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 1, "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.", "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. \n\nThe 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. \n\nAs 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. \n\nThis 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. \n\nFinally, 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. \n\nMinor comment: \nLine 402: “Llamma” -> “Llama”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EW27PRwZv6", "reviewer": "Reviewer_1Tc6", "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.", "questions": "I may have missed these points:\n\n(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?\n\n(2) How are the 3 question variants obtained? Are they paraphrases generated by LLMs?\n\n(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?\n\n(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.?\n\n(5) I am curious about the interpretability of the findings. Why do LLMs mimic human values when provided with Value Anchor prompts?\n\n(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?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 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.\n\nThe authors also demonstrate via different prompts that LLMs can consistently mirror psychological value traits of a certain population of humans.\n\nThe presentation uses clear figures and concise writing. The background knowledge on value measurements is sufficiently introduced, making the paper easy to follow.", "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.\n\n\nIn 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?\n\n\nThat 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.\n\n\nAlso, 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PJQqxMK9qk", "reviewer": "Reviewer_8MQX", "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.", "questions": "What do you consider to be the paper's main contributions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written and easy to follow.\n2. The work is well-grounded in established psychological theory, particularly Schwartz's Theory of Basic Human Values.\n3. The use of value rankings and correlations provides concrete, measurable ways to compare LLM outputs to human data.\n4. The paper studies a timely and important issue.", "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.\n\n2. 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.\n\n3.  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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hxRDtyrD7Y", "reviewer": "Reviewer_uULc", "summary": "- Investigates whether large language models (LLMs) exhibit consistent value structures similar to those found in humans, using well-established psychological frameworks.\n\n- 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).\n\n- LLM responses, when prompted correctly (especially with the \"Value Anchor\" prompt), show high consistency with human value hierarchies, including correlations between values.\n\n- 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.\n\n- 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.", "questions": "na", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 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.\n\n- 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. \n\n(Kovač-2024) \"Stick to your role! Stability of personal values expressed in large language models.\"\n\n(Lee-2024) \"Language Models Show Stable Value Orientations Across Diverse Role-Plays.\"\n\n(Wang-2024) \"Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews.\"\n\n(Scherrer-2023) \"Evaluating the Moral Beliefs Encoded in LLMs\"", "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.\n\n- 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)