From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Authors: Dongjun Kang
Joonsuk Park
Yohan Jo
JinYeong Bak
Venue: EMNLP 2023 Main (2023)
Keywords: Schwartz Value Theory
Large Language Model
Human Behavior
Personality
Value Injection

Abstract: Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing. However, conducting large-scale surveys like the European Social Survey to solicit people's opinions on individual issues can incur prohibitive costs. Leveraging prior research showing influence of core human values on individual decisions and actions, we propose to use value-injected large language models (LLM) to predict opinions and behaviors. To this end, we present Value Injection Method (VIM), a collection of two methods---argument generation and question answering---designed to inject targeted value distributions into LLMs via fine-tuning. We then conduct a series of experiments on four tasks to test the effectiveness of VIM and the possibility of using value-injected LLMs to predict opinions and behaviors of people. We find that LLMs value-injected with variations of VIM substantially outperform the baselines. Also, the results suggest that opinions and behaviors can be better predicted using value-injected LLMs than the baseline approaches.

From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Dongjun Kang
Joonsuk Park
Yohan Jo
Jin Yenne Bak
Sungkyunkwan University, Suwon, South Korea University of Richmond, VA, USA Seoul National University, Seoul, South Korea ehdwns2356eskku. edu, parké org. yohan. jo@snu. ac. kr, jy. bak@skku. edu
Abstract
1999; Bardi and Schwartz, 2003). For instance, the Schwartz value theory (Schwartz et al., 2012) speci- fies ten values- -ucsh as security and achievement- that are central to human life. Since these values are more manageable to collect from people than their opinions on every issue of our interest, we seek to predict people's opinions and behaviors based on their core values. More specifically, we propose to inject a target value distribution large language models (LLMs) and have them predict the opinions and behaviors of people with similar value distributions.
Being able predict people's opinions on is- sues and behaviors in realistic scenarios can be helpful in various domains, such as poli- ties and marketing. However, conducting large- scale surveys like the European Social Survey solicit people's 's opinions on individual issues incur prohibitive costs. Leveraging prior research showing influence of core human val- ues on individual decisions and actions, we propose to value-injected large language models (LLM) to predict opinions and behav- iors. To this end, we present Value Injection Method (VIM), collection of two methods- argument generation and question answering designed to inject targeted value distributions into LLMs via fine-tuming, We then conduct series of experiments on four tasks to test the ef- fectiveness of VIM and the possibility of using value-injected LLMS to predict opinions and behaviors of people. We find that LLMs value- injected with variations of VIM substantially outperform the baselines. Also, the results sug- gest that opinions and behaviors can be better predicted using value- injeetd LLMs than the baseline approaches.!
From technical perspective, LLMs are pre- trained on large corpora and thus inherently lack personality (Huang et al., 2022). This is not only problematic for our application, but also for others like chatbots, where LLMs with particular personal- ities are desired. To this end, researchers have mea- sured cultural values embedded in LLMs (Arora et al., 2023) and investigated methods that simulate human behaviors (Aher et al., 2023), among others. However, the best of our knowledge, there has not been an attempt to inject full set of human values into LLMS and use for predicting opin- ions and behaviors of people with similar value distributions.
1 Introduction
The ability to reliably predict people's opinions on particular issues or how they would choose to behave in different real-life scenarios can be bene- ficial to numerous professionals, including politi- cians and marketers. To this end, there exist large- scale surveys soliciting opinions on various issues, such as European Social Survey (ESS). However, collecting opinions on individual issues this way is laborious and costly.
In this paper, we propose the Value Injection Method (VIM) for injecting specific value distribu- tions into LLMs. VIM consists of argument gen- eration (AG) and question answering (QA). The AG method aims to inject values by training LLMs to generate opinions on issues consistent with the targeted value distribution. The QA method, the other hand, trains LLMs to specify how similar they are to a given description of a person, on a 6- 6-point scale from "Not like me at all" and "Very much like me."
Luckily, studies on human values claim that people have a small set of core values, which af- fects the daily decisions and actions (Stern et al.,
We first verify the effectiveness of VIM (Sec- tion 5). We inject values into LLAMA (Touvron et al., 2023) using variations of VIM resulting in three value-injected LLMs: VILLAMA, VIL-
"Corresponding authors
'Code: https://gi thub. com/ ctt/ womm/donggunKNN//VI //www. http://wwwwwoorscccaaaaaasaeee

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AG, and VILLAMAQA. Then, their performances against prompt-based baselines on two tasks: value survey and argument generation. The experiment results demonstrate that LLMs trained via VIM outperform the baselines on both tasks and that the variation of VIM using both meth- ods is superior.
harm others and violate social expectations or norms.
Hedonism (Hed); Pleasure or sensuous grati- fication for oneself.
Power (Pow): Social status and prestige, con- trol or dominance over people and resources.
Security (Sec): Safety, harmony, and stability of society and relationships.
We then test value-injected LLMs ability to pre- dict human opinions and behaviors (Section 6). In particular we investigate the following questions: Can a inntee-deeeeed LLM predict the behavior of a person with the same value distribution in a re- alistic situation? and Can a mulue-injected LLM predict the stance of a person with the same value distribution on political, social and other issues? The experiment results show that the answers to both questions are true to degree. In the behavior prediction task, predictions of VILLAMA show a substantial alignment to the gold standard be- haviors, achieving an average of 0.071 normalized mean squared error (NMSE). In the opinion pre- diction task, VILLAMA A achieves 0.099 NMSE, significantly outperforming the baselines ranging from 1.7 to 0.221.
Self-Direction (SD): Independent thought and action-choosing, creating, exploring.
Stimulation (Sti): Excitement, novelty, and challenge in life.
Tradition (Tra): Respect, commitment, and acceptance the customs and ideas that one's culture or religion provides.
Universalism (Uni): Understanding, appreci-
ation, tolerance, and protection for the welfare of all people and for nature.
The Schwartz value theory is an appropriate framework for representing human personality in our study. It provides a comprehensive understand- ing of individuals and groups by considering mul- tiple values. For example, research has shown that Chinese shopper tourists make purchases of items aligned with specific values, such as passion and jewelry (Choi et al., 2016). Additionally, peo- ple's prioritized values play role in their political decisions, including voting (Sagiv and Schwartz, 2000; Caprara and Zimbardo, 2004). Furthermore, Bonetto et al. (2021) explored the relationship between values and people's opinions regarding movement restrictions and social distancing mea- sures in the context COVID-19.
Our contributions are threefold:
We propose the novel problem of predicting human behaviors and opinions with specific values.
We present Value Injection Method (VIM), an effective method for injecting desired values into LLM.
We demonstrate that value-injected LLMs out- perform the baselines in predicting the behav- iors and opinions of people who have similar value distributions their target value distri- butions.
Personality theories, which seek to comprehend human behavior and cognition, have been em- ployed in the realm of Natural Language Process- ing (NLP) research. Lately, there has been esca- lating interest in exploring the utilization of these theories within generative language models, with the purpose of generating sentences that closely re- semble human-like language. Those studies aimed to identify the MBTI type and human value scale of LLMs by prompting them to answer question- naires like the MBTI questionnaire or Portrait Val- ues Questionnaire (PVQ) (Rao et al., 2023; Miotto et al., 2022). In addition to measuring personal- ity, there are studies that quantitatively measured whether prompting can induce desired personal- ity traits (Jiang et al., 2023; Caron and Srivastava, 2022).
2
Related Work
The Schwartz theory of basic values identifies ten basic human values that serve to characterize peo- plc's attributes:
Achievement (Ach): Personal success
through demonstrating competence according to social standards.
Benevolence (Ben): Preserving and enhanc- ing the welfare of those with whom one is in frequent personal contact.
Conformity (Con): Restraint of actions, in- clinations, and impulses likely to upset or

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Touch623-Valueee
Value Injection Method (VIM)
LLM
Argument Generation
output
Crozs Emtrpy Loss
Argument #1
Target Value Distribution
Conclusion:
Input prompt
I would say. "1...
Uni
We shoule
what would say
Ground-Truth output
L
S0
abancon the use school unifom
followingg statement.
Statement (condlusion)
Woulld say). (stanoa) because (premisej" about
Cont
Arsrase
(canclusion)
1ted
Premise:
Question Answering
Generate Answer
Sichool unfrrme are tco expersino and marry parents can the expense
Inpuf prompt
LLM output
Croes Entrppy Loss
Ach
Inniiaate for folliwing stoemeen
Becouse think...
whather
al", 2. ma?" "Bonwehee
lik mii"
Ground-Truth output
EqA
like me" 5. "Like "Vary much me? prion you. Statement: (stance) roondlus ont. (premise).
Because think (security) pimporsent to mej. my amsser is (93.
Security
Overview of Value Injection Method. It consists of two methods: argument generation and question ValueEval answering. Both methods utilize the Tau dataset to create prompts and ground ground-truth truth answer outputs. The LLM is trained by minimizing the cross-entropy loss between the L.LM's output given the input prompt and the corresponding ground- ground-truth output.
Figure 1:
Researchers have explored various techniques to guide language models in generating text that reflects specific personas or styles. For example, Rubin et al. (2022) conducted a study analyzing effective prompts in the in-context learning ap- proach, which enables the generation of desired sentences without fine-tuning the model. In another study, Ouyang et al. (2022) demonstrated excep- tional performance in tasks such learning from intended instructions and mitigating the generation of toxic output by utilizing reinforcement learn- ing with human feedback. This methodology helps align language models with human intent, thereby improving their ability to produce desired outputs. However, to the best of our knowledge, there has been no prior research investigating methods for injecting human values into LLMs.
y. the premise Va = (uAch, U iy if the value appears in the premise and oth- erwise. Table 12 shows an example of this dataset. We split the data with a ratio of 60:10:10 10 for train- ing.validation.tes..
U J. where
A Argument Generation (AG) This method in- jects into M by fine-tunining M to generate stances and premises that reflect V, for given conclusion. Algorithm I outlines the process, At a high level, we split arguments in the dataset into two groups. The first group is arguments that are likely to be made by someone who has V,, and the second group is arguments that are unlikely to be made by them. To be specifie, for cach argument and its values, we look at the corresponding value scores in V, and take the minimum score. If the min- imum score is greater than or equal to a threshold 7, then this argument is put into the first group; oth- erwise, the second group. The rationale is that the likelihood of an argument being made by person is bounded by their least prioritized value that is expressed in the argument. For the first group of ar- guments, the model is trained generate "I would say [argument]", whereas for the second group, "I would not say [argument]" (see Table 16 for the exact prompts). We use the cross-entropy loss LAG with next word prediction.
3 Value Injection Method (VIM)
To inject human values into LLM, we propose the value injection method (VIM) consisting of argument generation (AG) and question answering (QA). Suppose we inject target value distribu- tion Vi = -1,Ach Ben ...., ot Uni into a LLM M, where of ranges between and 6 (according to PVQ). For this, we use the Touché23-ValueHie dataset (Mirzakhmedova et al., 2023) consist- ing of value-related arguments. Each argument {Ca, Sa: Pa; Va} consists of conclusion, stance, premise, and values: the conclusion (c.,) represents a specific topic, the stance (sa)) indicates whether it is in favor of or against the conclusion, and the premise (pa) corresponds to the reasoning behind it. Each argument is labeled with values expressed in
Question Answering (QA) In contrast to AG, QA prompts LLM M to generate the stance and premise in relation the conclusion. The possible stances for each question are based on the six op- tions of the PVQ: "Not like me all", "Not like me", "A little like me", "Somewhat like me", "Like me", and "Very much like me", each associated

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Algorithm Argument Generation
Algorithm 2 Question Answering
Input
Input
1: Arguments A = (....... aN.
Arguments A {a,,..... .........00. aN)
2: Target group value distribution Vg (egch
Target group value distribution Vg tuget, Ben Con
uBen, iooo [
.g .'gg _ 3: Large language model M Training
9
3: Large language model M
Training
1: for Va A do
for Va A do do if e = then
2: Create empty list Lug
for Vua E V
3: for Vu, E V2 do
4: if r
then 5: Append vy into La
la = o/ 1 //11+ Bernoulli(v; % 1)
Make a GT answer with 2, la
6: end if
6: Make a prompt prma with Ca Sa, Pa
7: end for
7: Generate an answer from M by prm
8: if min [a) 2 then
8: Update the parameters of M by LQA
9: wa 'I would say"
9: end if
10: else
10: end for
Wa = would not say"
11: end for
end
Output
13:
Make GT argument with Ca. Sas Pas and wa
Trained target group LLM Mg
14: Make prompt prma with Co
15: Generate an argument from M by prma
Sti
16: Update the parameters of M by LAG
17: end for
Sec
Tra
Output
1: Trained target group LLM M3
Pow
Uni
with integers from 1 to 6, respectively.
Algorithm outlines the process. We use an argument a and the target value distribution V, for each iteration training a LLM M. As we will see in Section 4, a target value distribution can take real numbers as value scores. To map these scores to the six options in each question 21, 3, 4, 5, 6), if value score is not whole number, we rounded it down up to integer probabilistically based on its fractional part (c.g., if the score is 5.2, then it is rounded to with an 80% chance or to 6 with a 20% chance). We construct ground-trutte (GT) answer that first states a value associated with the argument, followed by the final choice l This allows us to update the parameters of the LLM M using the cross-entropy loss LQ^ by comparing the ground-truth answer with the answer generated by obtain trained M,
Hed
Ach
Con
Ben
Figure 2: Example of a target group value distribution. Score of indicates not consider the value at all, score of 6 correspond to the value being very important. This distribution shows relatively high tradition, simulation, and security. The value scores for this distribution are as follows: achievement-2.33, benevolence=2.1, confor- mity=2.9, hedonism=2.7, power=2.8, security=3.11 I, self- direction=2.1, stimulation=3.4, tradition=4.2. 4,2, universal- ism=2.7.
Experimental Sctup
4.1 Target Value Distributions
M. Through this process, generate appropriate answers for value-related questions based on the target value distribution Vg. Please refer to Table 17 for the exact prompt format the QA method.
For thorough evaluation of VIM, test various value distributions for injection, while ensuring that those distributions are realistic. To that end, we identified representative value distributions among humans using the European Social Survey (ESS)

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Section
Dataset
Prompt
Model Output
Ground Truth
Metric
$5.1
Portrait Values Questionmaire
Table 18
Number
Group value distribution
NMSE
$5.2
Eval
Table 19
Premise & Stance
Winning ratio
$6.1
VALUENET
Table 20
Agree or Disagree
Group value distribution Number
NMSE
$6.2
European Social Survey
Table 21
Number
NMSE
Table 1: Summary of experiments to demonstrate that VILLAMA has the ability of reflecting the value distribution consistently in various tasks." :-" denotes absence ground-truth data, so we conduct human evaluation where annotators determine the more appropriate generated argument between two different trained LLMs.
dataset. ESS is large-scale survey conducted ev- ery two years for individuals in Europe. As part of the survey, participants answer the Portrait Values Questionnaire (PVQ) (Schwartz, 2021), ) widely used questionnaire for profiling the respondent's value distribution according to Schwartz's s theory. 10-dimensional vec- for where each element represents the score of each value ranging between 1 (not at all) and (very likely).
Baselines ChatGPT Long) Modern decoder-based LLMs have shown impressive in-context learning perfor- mance (Brown et al., 2020; Mishra et al., 2022). We compare the performance of VILLAMA with three zero-shot prompting baselines that receive the target value distribution in the prompt:
(LLAMA Shorts LLAMA Long*
The resulting distribution
ALAMAShorli the pretrained LLAMA- ABB. It is given the target value distribution and the task description in the prompt. Please refer to Table 22 for the exact prompt;
To identify representative value distributions from ESS, we first computed the value distributions of 54,763 people from 28 European countries based on their responses to PVQ. Next, we clustered the distributions using K-means clustering, where each data point represents each person's 's value distribu- tion as a 10-dimensional vector. By applying the clbow method, we determined that 100 clusters are suitable (refer Appendix A for more details). Lastly, we took the average of the value distribu- tions each cluster, resulting 100 representative value distributions. In addition to them, we also included 28 value distributions that represent the 28 countries in ESS, by taking the average of value distributions for each country. Figure 2 shows one example of group value distribution. We train one LLM for each target value distribution and report the average score of all L.Mss.
2. Longis the same as LLAMA Shorts except the prompt now includes the defini- tion each value. Please refer to Table 23 for the exact prompt; and
3.
LongGs the same LLAMA except ChatGPT is used in place LLAMA.
4.3
Experiment Overview
We compare VILLAMA with baselines to demon- strate its ability in four tasks, as summarized in Table 1. For the evaluation of value injection itself, test:
how well its responses to PVQ recovers the target value distribution (Section 5.1)
how well it generates arguments that reflect the target value distribution (Section 5.2).
4.2 Models
For the evaluation of its ability to predict human behavior and opinions, we test:
Value-injected LLMs (VILLAMA, VIL-
LAMA AG, VILLAMAQA) Our value-injected LLAMA (VILLAMA) is LLAMA-7B (Touvron et al., 2023) fine-tuned on value injection tasks through Low-Rank Adaptation (LoRA) (Hu et al., 2022). The total loss function is the combination of AG and LQA. VILLAMA AG and VILLAMAQA are variations of VILLAMA trained for an ablation study. The former is LLAMA trained with the argument generation task only, and the latter, question answering.
how well it predicts whether people with the target distribution would conduct certain be- haviors or not in everyday situations (Sec- tion 6.1)
how well its responses to questions about spe- cific issues (c.g., political and religious topics) reflect the stance of people who have the target distribution (Section 6.2).

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Model
NMSE
Model
NMSE
Table presents the results of the PVQ evaluation. VILLAMA generates survey responses that better align with the target value distribution than other baselines. LLAMA exhibits the highest NMSE value, indicating a lack of align- ment with the target value distribution. Baselines with longer prompts achieve lower errors, demon- strating the efficacy of adding value definitions in the prompt. However, the performance is still not significantly better, even for ChatGPT, which a much larger model. Example results of this task are provided in Table 24.
Results
LLLAMA A Short
0.196
VILLAMA AG
0.182
LLAMA Loog
0.189
VILLAMAQA
0,053
ChatGPT Long 0.146
VILLAMA
0.034
Table 2: NMSE between the model's value distribution and the target value distribution averaged across 128 target value distributions. A lower average NMSE score indicates a higher alignment between the model's value distribution and the target value distribution. The best performance represented in bold, while the second best performance is represented with underlined.
With regard to the ablation study for VILLAMA, using both the AG and QA methods achieves the best performance, and simply training only with the AG method results in the worst performance. When trained using the QA method-whit is similar the PVQ task in format the performance better than the AG method only, but still falls behind VIL- LAMA. In addition, to verify the effectiveness of VIM. we adopt paired t-test that shows how much the method affects the results. We compared the re- sults of LLAMA Long and VILLAMA on value sur- vey. For the value survey, VILLAMA's 's improve- ment over LLAMA is statistically significant (p < 0.001).
5 Experiment 1: Value Injection
LLMs that have successfully been injected with a certain value distribution should be able to reflect the value distribution consistently in various scenar- ios and tasks, such as, in a value-profiling survey and argumentation.
5.1 Evaluation 1: Value Survey
One straightforward approach testing the suc- cess of value injection comparing model's self-reported value distribution with the target value distribution injected into the model. Since PVQ (Schwartz, 2021) is the most widely used survey for measuring people's value distribution (based on the Schwartz value theory), we prompt value-injected LLMs answer the PVQ questions. Please refer to Table 14 for example questions of PVQ.
5.2 Evaluation 2: Argument Generation
For the second evaluation, we test LLMs' ability to generate arguments that reflect the target value distribution, since examining opinion is one of the ways to reveal human values (Bergman, 1998; Hoff- man and Slater, 2007), and argument a means to express opinions. Determining whether a given argument reflects target value distribution is dif- ficult to automate. Thus, we ask human judges to make the call.
Setup We created PVQ prompts for this task fol- lowing the template shown in Table 18. Using these prompts, we instruct LLMs to select one of the six possible responses for each survey question these responses indicate the degree of similarity between the respondent and the description in the question, from 'Not like me at all' to 'Very much like me'. Once finished, we compute the value distribution from the responses according to the formula speci- fied by PVQ.
Setup First, we randomly selected 40 from total of 128 target distributions. Within each tar- get, we sampled two conclusions from the test set of the Touché23-Valueevva dataset (see the first paragraph of Section for the description of this dataset). For each conclusion, we prompted each LLM to generate a stance and a premise based on the target value distribution.
We introduce a metric called Normalized Mean Squared Error (NMSE), which represents the dif- ference between the normalized (between 0 and 1) predicted value scores Y, and the normalized target value scores Y, Smaller NMSE indicates closer alignment between the predicted and target value scores. The process was repeated for 128 tar- get value distributions (obtained in 14.1) and the average is reported.
Then, three human annotators were presented with two arguments- generated by two different LLMs with prompting and VIM for the same target value distribution, and asked to determine which argument better reflects the target value distribution. When unsure, they
and premise-

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situation? Schwartz (2013) examines the relation- ship between values and behavior in real-world situations.
VILLAMAAG
Setup VALUENET (Qiu et al., 2022) is a dataset derived from the SOCIAL-CHEM-IO dataset (Forbes et al., 2020), which contains various be- havioral patterns observed in everyday life. Each of the 21,374 scenarios in VALUENET is tagged with one value from the Schwartz value theory and spec- ified as having "Positive", "Negative", or "Un- related" relationship with the given value. Please refer to Table 13 for examples of scenarios from VALUENET.
VILLAMAQA
42.1%
33.8%
24.2%
VILLAMA
52.9%
15.4%
31.7%
25%
50%
75%
100%
Figure 3: Human evaluation results showing the per- centage of annotators selected between VILLAMA and LLAMA Long. Annotators were asked to select which of the argument generated using VILLAMA and LLAMAAong closer to the arguments generated by the target group. Among the methods, VIL- LAMA showed the highest win ratio.
We construct a test scenario set from VAL- UENET by randomly selecting a total of 500 sce- narios with 50 scenarios (25 positive and 25 nega- tive) for each of the 10 values. A LLM is prompted with test scenario and asked if it would behave the same way. It should answer either "agree" or "disagree". Please refer to Table 20 for the prompt template. The agreement ratio the percentage of cases in which the LLM either agrees in posi- tive scenarios or disagrees in negative scenarios. across all test scenarios. We calculated the NMSE between the re-scaled target value (ranging from 0 to 1) and the agreement ratio.
were allowed to select "I don't know." A total of 10 graduate students fluent English served as anno- tators after learning the Schwartz value theory. The inter-annotator agreement, measured using Fleiss' kappa (Fleiss, 1971), among the annotators was 0.54.
Results Figure presents the win, lose, and tie results for the variants of VILLAMA against ALAMALLog. Both VILLAMA and VIL- LAMAQA exhibit similar win ratios, but VIL- LAMA AG demonstrates a higher lose ratio com- pared to VILLAMAQA. This indicates that VIL- LAMA generates arguments that reflect the tar- get value distribution less effectively than VIL- LAMAQA does. Note that VILLAMA achieves the highest win ratio, indicating that when trained for both value injection methods, the target value distribution is injected into the LLM more reliably. Example results of this task are provided in Ta- ble 12.
Results Table 3 presents the results of the behav- ior prediction task. Overall, VILLAMA generates answers that align with the target value distribu- tion more effectively than the other baselines. This suggests that VILLAMA can predict human be- havior in everyday life situations more accurately based on the value distribution. However, for a few values, such Benevolence, Hedonism, and Tradition, LLLMALong achieved the best perfor- mance. Paired t-test results of LLAMA Long and VILLAMA varied by value; the improvement was statistically significant for Achievement and Self-direction (p < 0.001), but no significance was found for the other values. Interestingly, LLAMA Short and LLAMA Long showed lower mean eπror than ChatGPT Long: even though they smaller models and LLAMA has less in- formation in the prompt. Example results of this task are provided Table 26.
6 Experiment 2: Opinion & Behavior Predictions with Value-injected LLMs
Given value-injected LLMs, evaluate their abil- ity predict human behaviors everyday scenar- ios and opinions on various issues based on the underlying value distribution.
6.2 Opinion Prediction
6.1 Behavior Prediction
In this section, we tackle the question: Can value- injected LLM predict the stance of a person with the same value distribution on political, social and other issues? contrast to the behavior prediction
this section, we investigate the question: Can value- injected LLM predict the behavior of a per-
son with the same value distribution in a realistic

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Model
Ach
Ben
Con
Hed
Pow
Sec
SD
Uni
Ave.
LLAMAshort
0.092
0.180
0,082
0.091
0.031
0.138
0.095
0.065
0.086
0.179
0.104
LLAMA Loog
0.075
0.118
0.057
0.049
0.030
0.098
0.091
0.050
0.069
0.095
0.073
ChatGPT Long
0.092
0.251
0.177
0.061
0.032
0.187
0.058
0.075
0.179
0.156
0.127
AILLAMA
0.161
0.247
0,106
0.123
0.044
0.206
0,082
0.097
0.140
0.151
0.137
VILLAMAQA
0.065
0.127
0.054
0.053
0.029
0.101
0.075
0.058
0.084
0.109
0.075
VILLAMA 0.049
0.125
0.056
0.052
0.035
0.097
0.074
0.049
0.072
0.094
0.071
Table 3: Results for the behavior prediction task. The difference between the model and the target group's 's real-life
behaviors was quantified using normalized mean squared error (NMSE), where lower values indicate better prediction of the target group's s behavior. The best performance is represented in bold, while the second best performance is represented with underlined. VILLAMA (ours) outperforms other baselines.
Model
MST
PSWB
POL
UD
Ave.
questions.
LLAMA Short
0.197
0.148
0.384
0.153
0.221
Understanding of Democracy (UD): Stance on various issues in the democratic system, 45 questions.
LLAAM A Long
0.079
0.115
0.281
0.072
0.137
ChaiGPT Long
0.222
0.133
0.106
0.216
0.169
VIL.LAMA
0.106
0.078
0.272
0.238
0.174
VILLAMAQA
0.067
0.069
0.210
0.052
0.099
We created prompts for this task using the tem- plate in Table 21. We evaluated a given LLM's s ability to predict opinions specific issues by comparing its responses the actual responses by the group whose value distribution was targeted.
VILLAMA
0.061
0.059
0.139
0.140
0.099
Table 4: Results for each method for the ESS evaluation. The overall average NMSE for the four chapters. The best performance is indicated in bold, while the second best performance is indicated with underlined. Over- all, VILLAMA (ours) achieves the best performance in predicting specific issues based on the group value distribution.
Note that ESS questions use diverse response scales, including binary responses (0 or 1) and de- grees of agreement (0 to 10). We rescaled response scores to the range of 0 to to prevent certain ques- tions from having greater impact on the NMSE.
task targeting everyday life scenarios, this task con- cerns various issues, such as political, social, and religious ones.
Results Table 4 shows the results for the opin- ion prediction task. Overall, VILLAMA generates answers that align with the target value distribu- tion more effectively than the other LLMs; VIL- LAMA achieves the best or second-best best perfor- mance for four chapters, and it also exhibits the lowest average. These results suggest that VIL- LAMA can predict human opinions issues more accurately based on the value distri- bution. Also, LLAMAshort exihibits noticeably worse performance than LLAMA Longs indicating that including value definitions in the prompt has a significant impact on the outcome. In the paired t- test results of LLAMA and VILLAMA, VIL- LAMA's 's improvement over LLAMA Long is sta- tistically significant (p 0.001) in MST, PSWB, and POL. In addition, observe the tendency of ChatGPT Long to avoid answering questions related to opinion prediction. Further analysis of Chat- GPT's tendency to refuse to answer is in Appendix E.3. Example results of this task are provided Table 27.
Setup In this experiment, we utilized a subset of the ESS, excluding the PVQ. ESS consists of each respondent's ' demographic information, such as gender, age, and family relationships, and survey questions in various topics, such as Understanding Democracy, Digital Social Contacts, and Attitudes to Climate Change. We first excluded questions in ESS that are not common across the participating countries. Then, we asked L.LMs to answer the questionnaires in the following chapters in ESS:
specific
Media and Social Trust (MST): Media in- terest, beliefs and relationships with members of socicty, 5 questions.
Personal and Social Well-Being (PSWB): Personal emotions and life satisfaction such as depression, happiness, and achievement, 39 questions.
Politics (POL): Government, belief in the political system, opinions on immigrants, 34
"The response starts with "I cannot answer this question as it goes against the ethical guidelines of OpenAI."

---

7 Conclusion
with VIM can generate sentences that were not ac- tually spoken from them. This raises concerns, es- pecially for celebrities or public figures who share extensive personal information, as it may make them more susceptible to vulnerabilities such as the dissemination of fake news through misuse. To address this issue, employing discriminator that can distinguish between speech generated by an LLM trained values through VIM and authen- tic speech of individuals could be considered as a preventive measure.
In this paper, we introduced the Value Injection Method (VIM), which allows for the injection of specific value distributions into existing LLMs through argument generation and question answer- ing tasks. To assess the effectiveness of VIM across various value distributions, we conducted evalua- tions on 28 country groups and 100 social groups. The evaluations involved answering value surveys and generating arguments based on the given value distribution. Our results demonstrate that VIM out- performs other prompting methods in these evalu- ations. Additionally, we examined the efficayy of value injection and its ability to predict human be- havior through behavior prediction and opinion pre- diction tasks. The empirical experiments conducted these evaluation tasks confirm the effectiveness of VIM in value injection and its superior perfor- mance compared to other prompting methods in predicting human behaviors.
In our human evaluation process, we ensure that annotators are compensated more than the mini- mum wage.
Acknowledgements
We would like to thank the anonymous review- ers for their helpful questions and comments. This project is partially supported by Microsoft Re- search Asia. This work was partly supported by In- stitute of Information & communications Technol- ogy Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) 20200000--,,,,,, Abductive inference framework using omni-data for understanding complex causal relations & ICT Creative Consilience program (ITTP-2023-2020-0- 018)). And this work was partially supported by the New Faculty Startup Fund from Seoul National University.
Limitations
For VILLAMAAG. we set a fixed hyper-parameter parameter 7, which serves as the threshold for selecting the likelihood of the answer as three. The chosen num- ber is intuitive, considering that the score range of Schwartz values is from one to six. However, appro- priate y value may vary depending on the specific value distribution. While VIM demonstrates supe- rior performance compared to other baselines in various value-related tasks, further improvements could be achieved by exploring the effectiveness of different values for
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C Few-shot Results
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For the opinion prediction (ESS) task among the evaluation tasks, we conducted experiments not only for zero-shot but also for additional few-shot prompting and few-shot prompting using the Chain of Thought (CoT). We experimented with 1, 2, and 5 examples, the input context window of the LLM limited from conducting experiments with larger number of examples. Few-shot examples were randomly selected from the ESS dataset, and the prompts were carried out in the same manner as the zero-shot setting. The results are presented in the following table 5 and 6.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux. Timothée Lacroix, Baptiste Rozière, Naman Goyal, Fric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13977
Appendix
A
The number of Clusters
For value survey (PVQ) task, we conducted ex- periments with the version known for having larger number of questions and being more accu- rate, which consists of 40 questions. However, the datasct we have is based on 21 -questionn version, which has lower number of questions and lower accuracy. In this case, since we would have had to arbitrary assign answers to the remaining ques- tions, were unable to conduct few-shot experi- ments. The Behavior prediction task is answering with "agree" or "disagree" regarding whether the model would engage in the same action as a spe- cific value-related scenario. Our evaluation focuses not on individual answers, but on assessing the per- centage of "agree" or "disagree". Similar to Value survey task, there is challenge of assigning arbi- trary answers for few-shot examples, so we were unable conduct few-shot experiments.
240000
220000
200000
180000
160000
140000
50
100
150
Number Cluster
Figure 4: Elbow method graph. At the point where the number of clusters reaches 100, it is observable that the graph exhibits a curvature.
To determine the appropriate number of clusters, we employ the elbow method (Thorndike, 1953). Figure presents the results of this analysis. We observe a curvature in the graph when the number of clusters reaches 100, indicating a potential elbow point. So we set the number of social groups to 100.
We found that VILLAMA with VIM applied achieved a significantly lower average normalized mean squared error (NMSE) of 0.099 compared to both few-shot and few-shot CoT settings. In the few-shot experiments, both LLAMA Long and ChatGPT showed their best performance in the zero-shot setting. In the few-shot CoT experi- ments, LLAMALong showed the best performance with 5-shot, while ChatGPT performed best with 1-shot.
B Implementation Details
We train LLAMA-77 7B (Touvron et al., 2023) which has a parameter size of seven billion using Py- torch NVIDIA RTX A6000 GPU, with 48GB dedicated memory. We AdamW opti- mizer (Loshchilov and Hutter, 2019), train 5 epochs for fine-tuning, set batch size as 4, learning rate as 2e-5. We set the rank of LoRA, using the decom- position matrix to 8 and set the of Argument
/https://help...o openaappppppii....mmmmom raas433-chaatpt--eleess----e

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Model
Example
MST
PSWB
POL
UD
Ave.
Temp.
Value Survey
Behavior Pred.
Opinion Pred.
O-shot
0.079
0.115
0,281
0.072
0.137
0.2
0.033
0.072
0.102
I-shot
0.154
0.109
0.533
0.250
0,261
LLAMA
0.4
0.033
0.072
0.102
2-shot
0.118
0.117
0.271
0.334
0,210
5-shot
0.165
0.109
0.209
0.103
0.147
0.6
0.034
0.074
0.101
0.8
0.034
0.071
0.100
O-shot
0.222
0.133
0.106
0.216
0.169
I-shot
0.211
0,265
0.257
0.237
0.243
1.0
0.034
0.071
0.099
ChatCiPTzang
2-sbot
0.174
0.275
0.280
0.263
0.248
5-shot 0.174
0.299
0,284
0.282
0.259
Table 7: Results of temperature adjustment: The NMSE for the Value survey, behavior prediction, and opinion prediction tasks calculated for temperatures of 0.2, 0.4, 0.6, 0.8, and 1.0 when top-p is fixed at 0.5. The best performance is indicated bold.
VILLAMA
0-shot
0,061
0.059
0.139
0.140
0.099
Table 5: Results of few-shot experiments for the ESS evaluation. The overall average NMSE for the four chapters. The best performance is indicated in bold, while the second best performance is indicated with underlined. Overall, ViLLAMA(Our)) achieves the best performance in predicting specific issues based the group value distribution.
Top.
Value Survey
Behavior Pred.
Opinion Pred.
0.25
0.033
0.071
0.100
0.50
0.034
0.071
0.099
0.75
0.033
0.073
0.101
Model
Example
MST
POL
Ave.
Table 8: Results of top-p adjustment: The NMSE for the
O-shot
0.066
0.040
0.573
0.056
0.184
Value survey, behavior prediction, and opinion predic- tion tasks are calculated for top-p of 0.25, 0.50, and 0.75 when temperature is fixed at 1.0. The best performance indicated in bold.
1-shot
0.069
0.257
LLAMAromy
2-shot
0.103
0.069
0.432
0.109
0.178
5-shot
0.195
0.092
0.084
0.161
0.133
D-shot
0.611
0.301
0,302
0.198
0.353
I-shot
172
0.222
0.239
0.219
0.214
ChatCiPTz.ng
2-sbot
0.668
0.249
0.238
0.230
0,346
5-shot
0.186
0.264
0,243
0.239
0.233
formance was observed in the value survey and behavior prediction tasks at the lowest top-p value of 0.25, while the opinion prediction task achieved the highest performance at top-p value of 0.50. However, both results indicated that when temper- ature and top-p were adjusted, the difference in NMSE was less than 0.005, suggesting that these adjustments did not have a significant impact on the results.
VILLAMA
O-shot
0.061
0.059
0.139
0.140
0.099
Table Results few-shot Chain-of-Thoon Thouught (CoT) experiments for the ESS evaluation. The overall average NMSE for the four chapters. The best performance is indicated in bold, while the second best performance is indicated with underlined. Overall, VILLAMA(our)) achieves the best performance in predicting specific issues based the group value distribution.
E Additional Analyses
Temperature & Top-p Adjustment
This section describes the additional analyses con- ducted throughout the experiment and evaluation process.
To investigate how temperature and top-p affect the NMSE of each of the three evaluation tasks: value survey, behavior prediction, and opinion prediction, we conduct the experiment in which we adjusted both temperature and top-p.
E.1
Results of Evaluation 2: Argument Generation
Table presents the results of temperature ad- justment. We conducted experiments by varying the temperature values to 0.2, 0.4, 0.6, 0.8, and 1.0 while keeping the top-p fixed at 0.5. The lowest NMSE was observed at 0.2, which corresponds to the lowest temperature for value survey tasks. However, for behavior and opinion predictions, the NMSE is lowest at the highest temperature of 1.0. Table presents the results of adjusting the top- P parameter. We conducted experiments by vary- ing the top-p values to 0.25, 0.50, and 0.75 while keeping the temperature fixed at 1.0. The best per-
In Figure 3, which is the result of Evaluation 2: Argument Generation, we proceeded conduct an additional analysis addressing the following ques- tions.
First, why does VILLAMAQA worse than base- line? This is because of the prompts used for AG method in VIM are constructed with only two pos- sibilities: whether they "would say the (argu- ment]" "would not say the (argument)" the target value distribution. This approach can be chal- lenging to learn the value distribution properly. On the other hand, in the case of the QA method, it is

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learned with six appropriate answers corresponding different values. As result, can be considered that it learns the value distribution relatively better. "Assisted suicide should be criminal offense"
Model
Argument (Generated)
LLAMAramg tony
agree. think is not a good idea to kill somcone.
VILLAMAQA
ugree. Assisted suicide should be a criminal offence because it a form of murder.
VILLAMA
1 agree, Assisted suicide should be criminal offence because it against the law to take another person's life.
Table 9: Argument generated by LLAMA Long. VILLAMAQ and VILLAMA for topic "Assisted suicide should be a criminal offense". The value scores of the target value distribution used in the generation are as follows: 3chievement=2.3, benevolence=2.1, conformity=2.9, hedonism=2.7, power=2.8, security=3.1, self-direction-2.1, stimulation=3.4 4, tradition=4.2, universalism=2.7.
"We should legalize selection"
Model
Argument (Generated)
LLAMA Long
I aggre. It is a good way to prevent the gender imbalance.
VILLAMAQA
I Lagree. It is natural process and is not harmful to anyone.
VILLAMA
agree. Sex selection is natural right of every individual.
Table 10: Argument generated by LLAMA Long- VILLAMAQQ: and VILLAMA A for topic "We should legalize sex selection". The value scores of the target value distribution used in the generation are as follows: achievement-4.2.. benevolence=1.5, conformity=3.9, hedonism=2.2, power=5.0, security=1.7, self-direction=2.0, stimulation=3.9, tradition=1.8. 1.8, universalism=1.6.
captures the characteristics of the target value dis- tribution, generating arguments closely related specific values. Due to these instances, the win ratio is higher for VILLAMA compared to when using only AG or QA, where it successfully identifies the context of specific values and generates relevant arguments.
Second, why does VILLAMA have a higher lose ratio compared to VILLAMAQA? VIL- LAMAQA has a relatively high tie ratio. This is because the VILLAMAQA generates arguments similar to the baseline. These are two argument generation examples for the topic "Assisted sui- cide should be criminal offense" and "We should legalize sex selection" using LLAMA VIL- LAMAQA and VILLAMA.
In the case of VILLAMA there are situations where it fails to consider other values within the value distribution when generating arguments asso- ciated with specific values. Table is an example of such a case and the target value distribution. For the topic "We should legalize sex selection," " VILLAMA generated an argument associated with the value "Stimulation" which shows high score of 3.9 within the value distribution. However, the LLAMA Long also generated argument related to the value "Power" which scored 5.0, another high-scoring value. However, as described above, VILLAMAQA often generates relatively similar arguments to LLAMA Long- so this difference is small, which can be considered to have low loss ratio in QA and relatively large loss ratio in VIL- LAMA.
Table shows the generated arguments of the target value distribution. "Tradition" score is the highest at 4.2. The arguments generated by the LLAMA and VILLAMAQA have looser connection with tradition and can be in- terpreted as aligning with other values such as benevolence or universalism. When comparing the LLAMALong and VILLAMAQA in the hu- man evaluation process of selecting arguments for groups with the target distribution, it becomes chal- lenging to make decisions. Therefore, when com- paring LLAMA Long and the VIL.LAM QA due to the presence of similar arguments, the tie ratio relatively lower lose ra-
E.2 Cluster Size and NMSE
can increase, leading tio as result. On the other hand, the argument generated by the full VILLAMA shows clear re- lationship to tradition, such as the basis "laws should not be violated".. VILLAMA effectively
We examined how each cluster size, which corre- sponds to the target value distribution, influences PVQ, behavior and opinion prediction tasks. The relationship between the cluster size and the NMSE for each task illustrated in Figure 5. The NMSE

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PVO
Valuenet
0.30
0.20
NASE
0.10
0.20
NMSE
0.15
0.05
0.10
0.10
0.05
0.05
0.00
0.00
500
1000
1500
2000
2500
500
1000
1500
2000
500
1000
1500
2000
2500
Num of individuals the group
Num individuals in the group
Num of individuals the group
Figure 5: The NMSE results for each cluster in the PVQ, behavior prediction (Valuenet), and opinion prediction (ESS) tasks are presented. The cluster sizes range from a minimum 511 to maximum of 2616.
MST
PSWB
POL
UD
Ave.
European Social Survey Table 15
NMSE
0.222
0.133
0.106
0.216
0.169
G
Prompts
Avoidance (%)
12.2
28.1
29.7
0.6
17.7
This section describes the prompts used to train
Table 11: The NMSE and avoidance response ratio in the opinion prediction task of ChatGPT.
LLMs by VIM. The prompts are as follows:
VIM Argument Generation prompt- Table 16
is commonly observed to be low in clusters with relatively large sizes, but in clusters with small sizes, sometimes measured to be high. This phenomenon appears to be attributed the fact that larger groups tend to exhibit a more pronounced common value distribution, lifestyle, or opinion, while in smaller groups, the influence of single member becomes more significant.
VIM Question Answering prompt Table 17
Prompts for four different tasks are provided:
PVQ task prompt Table 18
Argument Generation task prompt Table 19
VALUENET task prompt Table 20
ESS task prompt Table 21
E.3 ChatGPT Response Avoidance Ratio in Opinion Prediction
Furthermore, Table 22 presents the basic prompt used provide the target group with Schwartz value distribution, while Table 23 shows the in- context learning prompt, which includes the defini- tion of Schwartz values and the value distribution.
In the opinion prediction task, observed that ChatGPT sometimes responds with 'I can't swer the questions because I'm an AT language model.' Since NMSE was calculated excluding these responses, we investigated the extent of re- sponse avoidance and its impact. Table 11 shows the NMSE and response avoidance ratio of Chat- GPT in the opinion prediction task.
H Experiments Result Examples
This section describes the examples of experiment results. The examples are presented as follows:
PVQ task results Table 24
ChatGPT exhibited the highest response avoid- ance ratio in the 'Politics' of the ESS, at 29.7%, and the lowest avoidance ratio in "Understanding Democracy,' at 0.6%. These findings confirm that high avoidance ratios contribute to the observed low NMSE.
Argument Generation task results Table 25
VALUENET task results Table
ESS task results Table 27
Human Evaluation
This section presents the human evaluation con- ducted for the argument generation task. Since there is no ground- -ruth argument based on the value distribution, we utilize Google Form for the evaluation. A screenshot of the questionnaire be seen in Figure 6.
F Dataset Examples
This section presents the examples of datasets. We four datasets in this paper follows:
Vouchv23-ValueLival Table 12
VALUENET Table 13
Portrait Values Questionnaire Table 14

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Conclusion
Stance
Premise
Value
We should ahandon the
In favor of
School uniforms expensive and many parents not afford the extra expense.
Security
of school uniform
We should abolish the Against Olympic Games
Olympic Games should not be banned. They are the tissue that solidify the friendship between nations in the world and a re- minder of peaceful world which should be the world vision. We should adopt gender-neutral language because it avoids fending people with gender stereo-types.
Benevolence, Universalism
should adopt gender- neutral language
favor
Conformity, Uni- versalism
Table 12: Examples of Touché23- ValueEval Dataset. Conclusion specific topic, stance refers to the agreement or disagreement with the conclusion. The premise represents the reasons for the stance the conclusion, and value encompasses all the values expressed in the premise.
Value
Stance
Scenario
Achievement
Negative
I am the original loser.
Tradition
Positive
Not wanting my kids candy for breakfast
Universalism
Negative
I hate seeing posts about people who have their lives together because I don't
Table 13: Examples VALUENET Dataset. Value represents one of the Schwartz values, while stance indicates whether the scenario is positive or negative to the value. The scenario depicts everyday actions associated with the value.
Thinking up new ideas and being creative is important him. He likes do things in his own original way. He seeks every chance he can to have fun. It important him do things that give him pleasure He strongly believes that people should for nature. Looking after the environment important him.
Table 14: Examples of Portrait Values Questionnaire Item, The items depict individuals involved in actions associated with the respective value.
No
Items
Please to where is all important for democracy general and 10 extremely important for democracy general.
Is your group discriminated against by other grounds? Please answer "Yes" or "No". What extent do you think those who hold extreme political views in your country today prevented from expressing them openly? Please answer 0 to 10 where 0 is not at all and 10 is completely.
Table 15: Examples of European Social Survey Item. The item is comprised of inquiries pertaining to political and social issues.
VIM Argument Generation
Prompt
Tell me what you would say about the following statement.
Statement: (conclusion)
Answer:
Answer type
would say, [stance) with that because (premise). about [conclusion]
Answer type
would not say, [stance] with that because (premise). about (conclusion)
Table 16: VIM: Argument Generation Prompt. { represents the placeholder. {conclusion), [stance) and (premise) in the placeholder are element of the Touché23 ValueEval dataset. This prompt pertains to determining what answer generate for the conclusion. Answer type 1 signifies that the target group would express the argument, while answer type 2 indicates that the target group would not endorse or articulate the same argument. The choice between generating Answer type Answer type 2 is based on w, Algorithm 1.

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VIM
Prompt
Question Answering
Indicate for the following statement whether
"Not like
all",
2. "Not like me",
"A little like me',
4. 'Somewhat like me!.
"Liike me",
"Very much me' description of you.
Statement: (stance) with (conclusion). (premise).
Answer:
Answer Because think {value) is [EXPRESSION; my answer (score).
Table 17: VIM: Question Answering Prompt. [ represents the placeholder. I |conclusion), (stance) and ( [premise] in the placeholder are element of the Touché23- Touché23-Valuevval VvaueEval dataset. Examples Touché23-ValueEBe dataset are table 12. Value is one of the Schwartz values, and EXPRESSION is textual representation that aligns with numeric scale ranging from 1 to 6. (1: not important to me at all, 2: not important to me, 3: little important to me. 4: somewhat important me, important to me, 6: very much important to me). Prompt inquires about degree of similarity between the provided statement and one's own self, while the answer denotes the appropriate response based on a particular value.
Task PVQ
Prompt
Indicate following statement whether it
"Not like me all",
2. "Not like me'.
3. A little like me'.
4. "Stmewhat like me',
5. "Likee me',
6. "Very much like me'
as a description of you. Statement: (PVQ item).
Answer:
Table 18: Task Portrait Values Questionnaire Prompt. { represents the placeholder. (PVQ item) in the placeholder are element of the PVQ. Prompt asks how similar the given statement is to oneself, while the answer is one of the options in prompt. Examples of PVQ item are table and generation results are table 24.
Task Argument
Prompt 1 stance
Do you agree or disagree with (conclusion)? Answer:
Generation
Prompt premise
Explain detail why you (stance) with (conclusion).
Answer:
Table 19: Task Argument Generation Prompt. ( represents the placeholder. [conclusion); [stance) and (premise) in the placeholder are element of the Touché23- ValueeEvl dataset. Prompt serves the purpose of generating stance, while Prompt serves the purpose of generating premise. Examples of Vouucé23-VaalueEval dataset are table 12 and generation results are table
ValueEval
Task VALUENET
Prompt
Do you agree the behavior or expression in the following scenario similar you?
Please answer agree" "I disagree".
Scenario:
(VALUENET scenario).
Answer:
Table 20: Task 3- VALUENET Prompt. ] represents the placeholder. /VALUENET scenario is the actions of people related to values everyday life. The purpose this prompt is to generate responses that agree or disagree with whether one would engage in similar actions expressions to the given scenario. Examples of VALUENET dataset are table 13 and generation results are table 26.

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Task ESS
Prompt (ESS item).
Answer:
Table 21: Task 4 European Social Survey (ESS) Prompt Example. | represents the placeholder. (ESS item) means the questions of the European Social Survey, Prompt asks the ESS item, while the answer is one of the options in prompt or digit. Examples of ESS item are table 15 and generation results are table 27.
Short Prompt
Prompt Let's roleplay.
Value Score:
Achievement: (target Achievement score)
Benevolence: [target Benevolence score)
Conformity: (target Conformity score)
Hedonism: [target Hedonism score)
Power: [target Power score]
Security: ttarget Security score)
Self- Saf-Direction: (target Self- Direction score]
Stimulation: [target Stimulation score)
Tradition: (target Tradition score)
Universalism: [target Universalism score)
As this person, please
the following question.
You just have choose the answer, you don't have explain it. Please choose only one option, even if you'l
[Task Description]
Table 22: Short prompt for providing the target value distribution to LLM. ( represents the placeholder, and (target value score) means corresponding value score of target value distribution. { (Task Description) the task description encompasses individual prompts designated for each of the four tasks delineated in Section 4, which are utilized input for the respective tasks.

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Long Prompt
Prompt Let's roleplay.
will describe person who have values between and each value. means value not important at all and means the value is very much important to him.
Value Definition:
Achievement: values personal success through demonstrating competence according to social stan-
dards
Benevolence: values preserving and enhancing welfare of those with whom one frequent personal contact (the 'in-group")
Conformity: values restraint actions, inclinations, and impulses likely to upset or harm others and violate social expectations or norms
Hedonism: values pleasure or sensuous gratification for oneself
Power: values social status and prestige, control or dominance over people and resources
Security: values safety, harmony, and stability of society, of relationships, and of self
Sclf-diraction: values independent thought and action-choosing, creating, exploring.
Stimulation: values excitement, novelty, and challenge in life
Tradition: values respect, commitment, and acceptance of the customs and ideas that onc's culture or religion provides
Universalism: values understanding, appreciation, tolerance, and protection for the welfare of all people and for nature
Value Score:
Achievement: [target Achievement score)
Benevolence: [target Benevolence score)
Conformity: (target Conformity score)
Hedonism: (target Hedonism score)
Power: [target Power score]
Security: (target Security score)
Seff-irection: (target Self- Direction score]
Stimulation: [target Stimulation score)
Tradition: (target Tradition score)
Universalism: [target Universalism score)
As this person, please answer the following question. You just have choose the answer, you don't have explain it.
Please choose only one option, even you sure.
[Task Description]
Table 23: Long prompt for providing the target value distribution to LLM. ( represents the placeholder, and the variables to inserted input in the form of definitions of Schwartz values, we aim to elicit responses from the language model that are more similar to those of the target group, leveraging the enriched information.
placeholders are the same as those table 22. However, by providing additional
PVQ Item
Answer (Generated)
VILLAMA
Thinking up new ideas being creative is important to He
things own original way.
He seeks every chance he can to have fun. It important him to do things that give him pleasure.
4
thinks it is best do things traditional ways. important him keep up the customs he has learned.
2
LLA M A
Thinking up new ideas and being creative is important to him. He likes to do things in his original way.
5
seeks every chance can fun. important
things give him pleasure.
He thinks it is best to do things in traditional ways. It important to him to keep the customs he has learned.
4
Table 24: Example results of task 1 - PVQ. The results reflect the responses of VILLAMAand LLAMA Long- Score is ranging from to 6, to indicate the extent of resemblance between L.M and the items presented.

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Conclusion
Premise (Genenrated)
Stance (Genenrated)
VILLAMA
We should abandon the use of school uniform
School uniforms good way to make students feel equal
Against
Assisted suicide should be criminal
Assisted suicide should be criminal offence because it against the law to take another person's life
In favor of
offence
We should ban human cloning
I think that human cloning is good thing because it can help people who are need of organs.
Against
LLAMA Long
We should abandon the use of school It not necessary wear uniform go school uniform
In favor of In favor of In favor of I think that human cloning very bad thing because it against the law.
Assisted suicide should be criminal
I think that is not good idea kill someone
offence
We should ban human cloning
Table 25: Example results of task 2 Argument Generation. Applied the VIM and prompting methods to the value distribution from figure generate reasons and stances for the given topics. Two samples of each result example using VIM and the prompting method. ViL.LAMAgeneratee arguments that values custom and law in relation to tradition rather than prompting.
Scenario
Value
Stance
Answer (Generated)
VILLAMA
decided to make budget so that wouldn't go broke again
Achievement
Positive
Agree
Asking someone to respect
Conformity
Positive
Agree
Going on trip without my hest friend, even though we planned it together
Hcdonism
Negative
Disagree
LLAMA
decided make budget that I wouldn't 't go broke again
Achievement
Positive
Disagree
Asking someone to respect me
Conformity
Positive
Agree
Going on trip without my best friend, even though we planned it together
Hedonism
Negative
Agree
Table 26: Example results of task 3 VALUENET. The generated responses aim to determine whether LLM agree or disagree with engaging in behaviors similar to scenarios that have relationship with specific value and stance.

---

Question
Answer (Generated)
VILLAMA
How important of 0 to 10 where 0 is not at all important and 10 is extremely important.
you live country is governed democratically? Please tell me on a score
Please tell me important you think is for democracy general national elections are free and lair? Please unswer 0 to where 0 is not at all important for democracy in general and 10 is extremely imporiant for democracy in general.
Would you say it generally bad or good for your country's cconomy that people come live here from other countries? Please answer () to 10, where 0 means had for the economy and 10 means good for the economy.
LLAMA A Long
you live country is governed democratically' Please tell me on a score of 0 to 10 where 0 is not at ull important and 10 is extremelyy important.
How important
Please tell how important you think it is for democracy general that national elections free and fair? Please answer to where 0 not at all important for democracy in general and 10 extremely important for democracy in general.
Would you say it generally bad or good for your country's cconomy that people come live here from other countries? Please answer ( to 10, where 0 means had for the economy and 10 means good for the economy.
Table 27: Example results of task 4- ESS. Through each method, LLM generate responses corresponding the given question.

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[Group_ __]] Value Distribution
Achievement 2.2
Benevolence: : 1.6
Conformity 2.7
Hedonism 2.0
Power: 4.0
Security 2.0
Self-direction: 1.7
Stimulation 1.8
Tradition: 3.7
Universalism : 1.7
Q1. We should stop the development of autonomous cars
Agree, Autonomous cars will make people lazy and not care about the environment.
Agree, Self-driving will make people lazy and unhealthy because they will not exercise.
don't know (tie)
Q2. Assisted suicide should be criminal offence
Agree, I think that it is not good idea kill someone.
Agree, Assisted suicide violation of the right life, which fundamental right, stated in the Universal Declaration of Human Rights.
don't know (tle)
Figure 6: Example of Human Evaluation Question Form. Annotators solve the questions presented in the question box located at the bottom by examining the value distribution of the group positioned at the top. In regard to the conclusion stated in the question, they select the stance and premise among the two options that most similar the group possessing the corresponding value distribution.


Reviews:
[{"review_id": "6j6vfrzCdm", "reviewer": "Reviewer_vyUS", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer,\n\nWe would like to appreciate you for taking the time to review our research. Thank you for all your constructive comments.\n\nOur responses to the questions are as follows:\n> In Figure 3, it seems that VILLAMAag is even worse than the baseline model, moreover, the full VILLAMA actually loses more than the VILLAMAqa model. Any idea why? It can be helpful to provide more analysis on the labeling result and see in what cases the full VILLAMA model improves and in what cases becomes worse.\n\n> First question, why is ViLLaMA AG worse than baseline?\n\nThe prompts used for AG training are constructed with only two possibilities: whether they are “would say the {argument}” or “would not say the {argument}” to the target value distribution. This approach can be challenging to learn the value distribution properly. \nOn the other hand, in the case of the QA method, it is learned with six appropriate answers corresponding to different values. As a result, it can be considered that it learns the value distribution relatively better.\n\n> Second question, why does Full ViLLaMA have a higher lose ratio compared to ViLLaMA QA?\n\nIn Figure 3, ViLLaMA QA has a relatively high tie ratio. This is because the ViLLaMA QA generates arguments similar to the baseline. These are argument generation example results for the topic \"Assisted suicide should be a criminal offense,\" using LLaMA (baseline), ViLLaMA QA, and ViLLaMA.\n\nThis is the value distribution of target group.\n| Ach | Ben | Con | Hed | Pow | Sec |  SD | Sti | Tra | Uni |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 2.3 | 2.1 | 2.9 | 2.7 | 2.8 | 3.1 | 2.1 | 3.4 | 4.2 | 2.7 |\n\n| Topic      | Assisted suicide should be a criminal offence                                                                        |\n|------------|----------------------------------------------------------------------------------------------------------------------|\n| Baseline    | I agree. I think that it is not a good idea to kill someone.                                                         |\n| ViLLaMA QA | I agree. Assisted suicide should be a criminal offence because it is a form of murder.                               |\n| ViLLaMA    | I agree.  Assisted suicide should be a criminal offence because it is against the law to take another person's life. |\n\nIn the target distribution, \"Tradition\" score is the highest at 4.2. \nThe arguments generated by the baseline and ViLLaMA QA have a looser connection with tradition and can be interpreted as aligning with other values such as benevolence or universalism.\nWhen comparing the baseline and ViLLaMA QA in the human evaluation process of selecting arguments for groups with the target distribution, it becomes challenging to make decisions. Therefore, when comparing baseline and the ViLLaMA QA, due to the presence of similar arguments, the tie ratio can increase, leading to a relatively lower lose ratio as a result.\n\nOn the other hand, the argument generated by the full ViLLaMa shows a clear relationship to tradition, such as the basis of “laws should not be violated”.\nViLLaMA effectively captures the characteristics of the target value distribution, generating arguments closely related to specific values. Due to these instances, the win ratio is higher for ViLLaMA compared to when using only AG or QA, where it successfully identifies the context of specific values and generates relevant arguments.\n\nHowever, in the case of ViLLaMA, there are situations where it fails to consider other values within the value distribution when generating arguments associated with specific values. The following is an example of such a case and value distribution of target group.\n\n| Ach | Ben | Con | Hed | Pow | Sec |  SD | Sti | Tra | Uni |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 4.2 | 1.5 | 3.9 | 2.2 | 5.0 | 1.7 | 2.0 | 3.9 | 1.8 | 1.6 |\n\n| Topic      | We should legalize sex selection                                                                        |\n|------------|----------------------------------------------------------------------------------------------------------------------|\n| Baseline   | I agree. It is a good way to prevent the gender imbalance.                                                |\n| ViLLaMA QA | I agree. It is a natural process and it is not harmful to anyone.                               |\n| ViLLaMA    | I agree. Sex selection is a natural right of every individual. |\n\nFor the topic \"We should legalize sex selection,\" ViLLaMA generated an argument associated with the value \"Stimulation\" which shows a high score of 3.9 within the value distribution. However, the baseline also generated an argument related to the value \"Power\" which scored 5.0, another high-scoring value. \n\nHowever, as described above, ViLLaMA QA often generates relatively similar arguments to baseline, so this difference is small, which can be considered to have a low loss ratio in QA and a relatively large loss ratio in ViLLaMA.\n\nWe view these limitations as part of our future work and will continue to make efforts to improve performance. Additionally, we will describe these examples and analysis through an extra page.\n- - -\n> I am a bit concerned with the setting of the baseline. Since you are fine-tuning on the data, comparing with zeroshot prompting is not fair. fewshot prompting and fewshot CoT would be better baselines and are necessary.\n\nYes, we have taken into consideration the points you mentioned about the baseline. For the Opinion Prediction (ESS) task among the evaluation tasks, we conducted experiments not only for zero-shot but also for additional few-shot prompting and few-shot prompting using the Chain of Thought (CoT). We experimented with 1, 2, and 5 examples, as the input context window of the LLM limited us from conducting experiments with a larger number of examples. Few-shot examples were randomly selected from the ESS dataset, and the prompts were carried out in the same manner as the zero-shot setting. The results are presented in the following table.\n\n< Opinion Prediction (ESS) Few-Shot Results >\n|   Model  | Number of example |  MST  |  PSWB |  POL  |   UD  | NMSE Ave. |\n|:--------:|:-----------------:|:-----:|:-----:|:-----:|:-----:|:---------:|\n| LLaMA-7B |       0-shot      | 0.079 | 0.115 | 0.281 | 0.072 |     0.137 |\n|          |       1-shot      | 0.154 | 0.109 | 0.533 | 0.250 |     0.261 |\n|          |       2-shot      | 0.118 | 0.117 | 0.271 | 0.334 |     0.210 |\n|          |       5-shot      | 0.165 | 0.109 | 0.209 | 0.103 |     0.147 |\n|  ChatGPT |       0-shot      | 0.222 | 0.133 | 0.106 | 0.216 |     0.169 |\n|          |       1-shot      | 0.211 | 0.265 | 0.257 | 0.237 |     0.243 |\n|          |       2-shot      | 0.174 | 0.275 | 0.280 | 0.263 |     0.248 |\n|          |       5-shot      | 0.174 | 0.299 | 0.284 | 0.282 |     0.259 |\n|  ViLLaMA |       0-shot      | 0.061 | 0.059 | 0.139 | 0.140 | **0.099** |\n\n< Opinion Prediction (ESS) Few-Shot with CoT Results >\n|   Model  | Number of example |  MST  |  PSWB |  POL  |   UD  | NMSE Ave. |\n|:--------:|:-----------------:|:-----:|:-----:|:-----:|:-----:|:---------:|\n| LLaMA-7B |       0-shot      | 0.066 | 0.040 | 0.573 | 0.056 |     0.184 |\n|          |       1-shot      | 0.122 | 0.069 | 0.769 | 0.066 |     0.257 |\n|          |       2-shot      | 0.103 | 0.069 | 0.432 | 0.109 |     0.178 |\n|          |       5-shot      | 0.195 | 0.092 | 0.084 | 0.161 |     0.133 |\n|  ChatGPT |       0-shot      | 0.611 | 0.301 | 0.302 | 0.198 |     0.353 |\n|          |       1-shot      | 0.172 | 0.222 | 0.239 | 0.219 |     0.214 |\n|          |       2-shot      | 0.668 | 0.249 | 0.238 | 0.230 |     0.346 |\n|          |       5-shot      | 0.186 | 0.264 | 0.243 | 0.239 |     0.233 |\n|  ViLLaMA |       0-shot      | 0.061 | 0.059 | 0.139 | 0.140 | **0.099** |\n\nWe found that ViLLaMA with VIM applied achieved a significantly lower average normalized mean squared error (NMSE) of 0.099 compared to both few-shot and few-shot CoT settings.\nIn the few-shot experiments, both LLaMA-7B and ChatGPT showed their best performance in the zero-shot setting. In the few-shot CoT experiments, LLaMA-7B showed the best performance with 5-shot, while ChatGPT performed best with 1-shot.\n\nFurthermore, we conducted significance tests to verify the effectiveness of VIM. In the case of Few-shot, we compared ViLLaMA's results against LLaMA-7B's 0-shot results, and in the case of Few-shot CoT, we compared against ViLLaMA's results with LLaMA-7B's 5-shot results. We adopt a paired t-test that shows how much the method affects the results. \nFor the Few-shot and Few-shot CoT, ViLLaMA's improvement over LLaMA-7B is statistically significant (p-value < 0.001).\n\nFor PVQ task, we conducted experiments with the version known for having a larger number of questions and being more accurate, which consists of 40 questions. However, the dataset we have is based on a 21-question version, which has a lower number of questions and lower accuracy. In this case, since we would have had to arbitrary assign answers to the remaining questions, we were unable to conduct few-shot experiments.\nThe Valuenet task is answering with \"agree\" or \"disagree\" regarding whether the model would engage in the same action as a specific value-related scenario. Our evaluation focuses not on individual answers, but on assessing the percentage of \"agree\" or \"disagree\". Similar to PVQ task, there is a challenge of assign arbitrary answers for few-shot examples, so we were unable to conduct few-shot experiments.\n\nWe will use an extra page to present the results of these experiments as described above if accepted.\n- - -\n>  More ethical discussions are needed. I can see the value of the task, however, it can also create societal harm as you are predicting public opinions and values. Marginalized groups’ values may not be well representative in the data and it would be good to see the distribution regarding different groups of people.\n\nWe share the sentiment that the opinions of marginalized groups should not be misrepresented or ignored. Given the lack of metadata, however, we are unable to identify members of marginalized groups in our dataset. Thus, unfortunately, we cannot empirically show that their opinions are not ignored. However, we posit that their opinions are not ignored indeed. This is because members of a marginalized group would have various value distributions, as the Schwartz value theory is designed to capture various aspects of human values. (In fact, to think that the members of a particular group share similar value distributions across all ten dimensions of Schwartz value theory would be a stereotype of the group and should be avoided.) Following this logic, members of a marginalized group would have distinct value distributions, and the prediction of each member’s opinion would be affected by the (hypothetical) group of people sharing similar value distributions. Note, the members of this hypothetical group may belong to various groups, not a particular marginalized group in real life.\n\nStill, we think you brought up an important point, and we will include more ethical discussions in the paper.", "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": "1. A super interesting task\n2. The proposed method (at least VILLAMA qa) seems to be working", "reasons_to_reject": "1. Missing comparisons with several key baselines (e.g. few-shot prompting and few-shot CoT prompting)\n2. The proposed VILLAMA AG method does not seems to always work, more analysis on this is needed\n3. More ethical discussions are needed as the task is sensitive and might cause social harm.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes to use value-injected large language models (LLM) to predict opinions and behaviors. The author proposes Value Injection Method (VIM) which combines argument generation and question answering to inject targeted value distributions into LLMs via fine-tuning. Through a series of experiments, the authors found that the proposed models are significantly better than the baselines.\n\nOverall I think it is a very interesting task and I can definitely see its value. I have several questions, comments and concerns.\n1. In figure 3, it seems that VILLAMAag is even worse than the baseline model, moreover, the full VILLAMA actually loses more than the VILLAMAqa model. Any idea why? It can be helpful to provide more analysis on the labeling result and see in what cases the full VILLAMA model improves and in what cases become worse.\n2. I am a bit concerned with the setting of the baseline. Since you are fine-tuning on the data, comparing with zeroshot prompting is not fair. fewshot prompting and fewshot CoT would be better baselines and are necessary.\n3. More ethical discussions are needed. I can see the value of the task, however, it can also create societal harm as you are predicting public opinions and values. Marginalized groups values may not be well representative in the data and it would be good to see the distribution regarding different groups of people.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Ggd8powiEE", "reviewer": "Reviewer_FesA", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer,\n\nWe would like to appreciate you for taking the time to review our research. Thank you for all your constructive comments.\n\nOur responses to the questions are as follows:\n> The novelty of the paper is limited. The idea is mainly to leverage LLM through fine-tuning, which has been extensively studied before. No novel techniques are proposed.\n\nYes, as you mentioned, fine-tuning is a well-established technique. However, how to leverage it to effectively inject the value distribution into a model is an unexplored area, and we believe that finding an effective method out of many possible approaches is also a significant technical contribution. Just like instruction tuning is essentially fine-tuning but significantly impacted the paradigm of language models by the way the training data are designed, we proposed the argument generation (AG) and question answering (QA) methods rather than simply fine-tuning on existing labeled data. In each approach, AG and QA, we provided working solutions for how to select arguments and transform them into appropriate training data. This enables suitable learning for all conceivable value distributions. \n\nOur main contribution to this paper is not only proposing the Value Injection Method (VIM) but also predicting human behaviors and opinions using LLMs. We tackled the question of whether it is possible to predict the human behavior and opinions of specific groups using LLM, and we have shown the feasibility of value-injected LLMs in predicting them without directly training on the individual tasks. \nCompared to traditional methods like surveys, this approach has the significant advantage of being very cost-effective. There are various possibilities for applications such as policy formulation, demand analysis, marketing, and more.\n- - -\n> Could reproduce the results with some difficulty. The settings of parameters are underspecified or subjectively determined; the training/evaluation data are not widely available\n\nWe submitted the experimental code for reproduction and specified the hyperparameters in the shared code. However, we missed including this information in the paper. Thank you for bringing this to our attention. The LLMs used in the experiments are LLaMA-7B and ChatGPT (gpt-3.5-turbo). For LLaMA-7B, we used a temperature of default value, 1.0 and top-p of 0.5. For ChatGPT, we utilized the OpenAI API with a temperature of the default value, 1.0, and no further additional settings were applied. We will add these detailed parameter settings to the Implementation Details section of Appendix E. And we hope this will help with reproducibility.\n\nFurthermore, all datasets we used are publicly available and the reference of each dataset is already specified in our paper: Touche23-ValueEval, Valuenet, and the European Social Survey. Anyone interested can easily access these datasets and apply VIM using the described methodology. We will revise the writing to provide clearer instructions and additional explanations, making it even easier for others to follow the dataset and VIM procedure.", "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": "(1) The idea is interesting. \n(2) Extensive experiments have been done to prove its efficacy.\n(3) Te paper is well written and easy to follow. \n\n", "reasons_to_reject": "(1) The novelty of the paper is limited. The idea is mainly to leverage LLM through fine-tuning, which has been extensively studied before. No novel techniques are proposed. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposed to use value-injected LLMs to predict opinions and behaviors of people. two methods: argument generation and question answering are designed to inject targeted value distributions into LLMs via fine-tuning. \n", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "OZNq7mPGQM", "reviewer": "Reviewer_1Yni", "summary": "", "questions": "A. How do you set the hyper-parameters of each LLM, and how do the temperature and top-p influence the performance?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer,\n\nWe would like to appreciate you for taking the time to review our research. Thank you for all your constructive comments.\n\nOur responses to the questions are as follows:\n> How do you set the hyper-parameters of each LLM, and how do the temperature and top-p influence the performance?\n\nWe submitted the experimental code for reproduction and specified the hyperparameters in the shared code. However, we missed including this information in the paper. The LLMs used in the experiments are LLaMA-7B and ChatGPT (gpt-3.5-turbo). For LLaMA-7B, we used a temperature of default value, 1.0 and top-p of 0.5. For ChatGPT, we utilized the OpenAI API with a temperature of the default value, 1.0, and no further additional settings were applied. We will add these detailed parameter settings to the Implementation Details section of Appendix E. And we hope this will help with reproducibility. \n\nDuring the experiments, due to resource and time constraints, we intuitively set parameters such as gamma, learning rate, and the rank of LoRA, in addition to the previously mentioned temperature and top-p. We will conduct additional experiments to explore how top-p and temperature affect the performance, and we will describe the results in the final paper.\n- - -\n> There is a problem with the expression of contribution 1. I deem that the novel problems should emphasize predicting human behaviors and opinions with specific values instead of using value-injected LLMs.\n\nThank you for your advice. We believe that your suggestion clarifies our intended contribution even further. As advised, we will make the revision to the expression of contribution 1, changing it from \"using value-injected LLMs\" to \"with specific values.\"\n- - -\n> The experimental results shown in this paper need some significance tests to verify the VIM's effectiveness further.\n\nYes, we believe significance test is a useful way to demonstrate the effectiveness of VIM. To address the points you mentioned, we adopt a paired t-test that shows how much the method affects the results. We verified the effectiveness of VIM by comparing the results of LLaMA-7B and ViLLaMA on PVQ, Valuenet, and ESS tasks. \nFor the PVQ and ESS tasks, ViLLaMA's improvement over LLaMA-7B is statistically significant (p-value < 0.001). For the Valuenet task, it varied by value; the improvement was statistically significant for Achievement and Self-direction (p-value <= 0.001), but no significance was found for the other values. \nBased on these results, we will continue our efforts to enhance the performance in these tasks as future work and describe these results and explanations in the final paper.", "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": "1. The idea of injecting human values into LLM is quite novel.\n2. Detailed experimental analysis.\n", "reasons_to_reject": "1. There is a problem with the expression of contribution 1. I deem that the novel problems should emphasize predicting human behaviors and opinions with specific values instead of using value-injected LLMs.\n2. The experimental results shown in this paper need some significance tests to verify the VIM's effectiveness further.\n3. Missing the hyper-parameters of LLMs.\n", "questions_to_authors": "A. How do you set the hyper-parameters of each LLM, and how do the temperature and top-p influence the performance?", "paper_topic_and_main_contributions": "This paper focuses on predicting human behaviors and stances. To tackle this problem, the author proposes a value-injected large language model which can inject human values with the help of argument generation and question answering via LORA fine-tuning. The experimental results show that the proposed methods can properly inject human values and outperformers vanilla large language models like LLaMA and ChatGPT. The main contributions are three folds: 1) the authors introduce a novel problem to predict human behaviors and opinions with specific values; 2) the authors present a Value Injection Method (VIM) for injecting human values into LLMs; 3) the detailed experiments prove the feasibility of VIM.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None.", "Typos_Grammar_Style_and_Presentation_Improvements": "1. In line 285, missing right bracket."}, "extra_text": {}}, {"review_id": "YnKxeRAlLD", "reviewer": "Reviewer_uZ69", "summary": "", "questions": "algorithm 5, line 5: suggestion - add in line 2 that V_g={v_g^z}, so the connection is clearer. It wasn't clear to me, at first.\n\nalgorithm 2, line 4: can this be clarified? The text says \"we discretize the real numbers into integer l_a by considering the number of decimal places\", and that does not seem to fit all the parts of this assignment?\n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer,\n\nWe would like to appreciate you for taking the time to review our research. Thank you for all your constructive comments.\n\nOur responses to the questions are as follows:\n>  algorithm 5, line 5: suggestion - add in line 2 that V_g={v_g^z}, so the connection is clearer. It wasn't clear to me, at first.\n\nThank you for your advice. We appreciate your suggestion to add V_g={v_g^z} in line 2, as it would provide better clarity in understanding our algorithm. We will describe the algorithm reflecting this change.\n- - -\n> algorithm 2, line 4: can this be clarified? The text says \"we discretize the real numbers into integer l_a by considering the number of decimal places\", and that does not seem to fit all the parts of this assignment?\n\nThank you for pointing out this unclarity. We think the current explanation is a little bit hard to follow. We intended to use the Bernoulli distribution to prevent situations where rounding the value score always results in only one single score. If the value score is determined as a single score through rounding, there exists a problem where the score will always be lower or higher than the actual score, making it impossible to accurately represent it.\n\ne.g.)\n\n    Achievement score = 4.7\n    Score (using round): 5 (always)\n    Score (using Bernoulli distribution): 4 or 5 (4 = 30%, 5 = 70%)\n\nWe will revise the sentence \"we discretize the real numbers into integer l_a by considering the number of decimal places\" to \"If an average value score is not a whole number, we rounded it down or up to an integer probabilistically based on its fractional part (e.g., if the score is 5.2, then it is rounded to 5 with an 80% chance or to 6 with a 20% chance).\" with an example added to it for a clearer expression. Furthermore, we will continue to carefully examine the algorithm and review it to enhance clarity, ease of understanding, and reproducibility.", "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": "-- The paper is well-written and clear\n\n-- The explored topic is interesting and timely\n\n-- The experimental setting is sound, and the explored questions are interesting\n\n-- Results are convincing\n", "reasons_to_reject": "-- None", "questions_to_authors": "algorithm 5, line 5: suggestion - add in line 2 that V_g={v_g^z}, so the connection is clearer. It wasn't clear to me, at first.\n\nalgorithm 2, line 4: can this be clarified? The text says \"we discretize the real numbers into integer l_a by considering the number of decimal places\", and that does not seem to fit all the parts of this assignment?\n", "paper_topic_and_main_contributions": "The paper presents a method for Value Injection of human values into LLMs. The method uses data from the Touché23-ValueEval dataset of arguments, where each argument has a conclusion, stance, premise and values. The values are represented with a 10 dimensional binary vector, specifying whether the argument reflects or not one of the 10 human values in Schwartz values identify theory. The theory suggests that a human personality may be characterized by these 10 values.\n\nThe data is used to fine-tune a LAMMA model, by formulating argument generation and question answering tasks posed using the dataset.\n\nThe value-injected model ViLLaMA, is then evaluated in multiple experiments. The used baselines are variants of LLaMA without any fine-tuning, and variants trained only on argument generation or questions answering. Also included is a comparison with ChatGPT.\n\nThe presented experiments explore (1) the personality of the LLM, evaluated by taking the PVQ questionnaire, (2) the ability to generate arguments matching a given personality, (3) the ability to predict the behavior of people of a certain personality, and (4) whether its responses to questions about various issue (e.g. political issues) match the answers of a person with the same personality?\n\nResults for the experiments show that ViLLaMA is typically the top-performer on the above tasks, from the considered baselines.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "Yes", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Justification_for_Ethical_Concerns": "I think there might be ethical concerns with injecting human values into LLMs. There is some discussion in the paper on that, in the ethics statement section, but its not very thorough."}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "typos/grammar\n\nline 57: and investigated methods simulate human behaviors -> .. that simulate ... ?\n\nline 77: resulting three value-injected -> missing word ?\n\nline 86: to prediction opinions and behaviors -> to predict\n\nline 285: (refer to Appendix A for more details -> add )"}}]

Author rebuttal:

Meta-review: The paper introduces a method for injecting human values into large language models (LLMs) and focuses on predicting opinions and behaviors. The reviewers find the paper well-written (uZ69, FesA) and the topic of research exciting and timely topics (uZ69). Most reviewers acknowledge the novelty and interest in the paper's idea of injecting human values into LLMs and its potential societal impact. Reviewers vyUS and FesA commend the extensive experiments conducted to demonstrate the method's efficacy. Reviewer uZ69 appreciates the well-written and clear presentation of the paper and the sound experimental setting.

The reviewers have highlighted some concerns and suggestions. Reviewer vyUS raises concerns about the performance of the VILLAMAag model compared to the baseline and suggests a more in-depth analysis of the labeling results. They also recommend ethical discussions regarding potential societal harm. Reviewer FesA notes that the paper's novelty is limited, mainly relying on fine-tuning, and suggests the need for proposing novel techniques. Reviewer 1Yni suggests refining the expression of the contributions, conducting significance tests to verify further the effectiveness of the Value Injection Method (VIM), and including hyper-parameter details for LLMs. 

In summary, the reviewers collectively acknowledge the potential of the paper's approach. To further improve the article, address concerns related to performance analysis and include more experimental details to help future paper readers. Also, add a discussion about the ethical implications of the proposed approach.
Decision: Accept-Main