From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models (2023)

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.

## 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 beneficial to numerous professionals, including politicians and marketers. to this end, there exist largescale surveys soliciting opinions on various issues, such as european social survey (ess). 2 however, collecting opinions on individual issues in this way is laborious and costly. luckily, studies on human values claim that people have a small set of core values, which affects the daily decisions and actions @xcite . for instance, the schwartz value theory @xcite specifies ten values-such as security and achievementthat 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 to large language models (llms) and have them predict the opinions and behaviors of people with similar value distributions. from a technical perspective, llms are pretrained on large corpora and thus inherently lack personality @xcite . this is not only problematic for our application, but also for others like chatbots, where llms with particular personalities are desired. to this end, researchers have measured cultural values embedded in llms @xcite and investigated methods that simulate human behaviors @xcite , among others. however, to the best of our knowledge, there has not been an attempt to inject a full set of human values into llms and use it for predicting opinions and behaviors of people with similar value distributions. in this paper, we propose the value injection method (vim) for injecting specific value distributions into llms. vim consists of argument generation (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, on the other hand, trains llms to specify how similar they are to a given description of a person, on a 6-point scale from "not like me at all" and "very much like me." we first verify the effectiveness of vim (section 5). we inject values into llama @xcite using variations of vim resulting in three value-injected llms: villama, vil-lama ag , and villama qa . then, we test 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 methods is superior. we then test value-injected llms' ability to predict human opinions and behaviors (section 6). in particular we investigate the following questions: can a value-injected llm predict the behavior of a person with the same value distribution in a realistic situation? and can a value-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 a degree. in the behavior prediction task, predictions of villama show a substantial alignment to the gold standard behaviors, achieving an average of 0.071 normalized mean squared error (nmse). in the opinion prediction task, villama achieves 0.099 nmse, significantly outperforming the baselines ranging from 0.137 to 0.221. our contributions are threefold: ## related work the schwartz theory of basic values identifies ten basic human values that serve to characterize people's attributes: the schwartz value theory is an appropriate framework for representing human personality in our study. it provides a comprehensive understanding of individuals and groups by considering multiple values. for example, research has shown that chinese shopper tourists make purchases of items aligned with specific values, such as passion and jewelry @xcite . additionally, people's prioritized values play a role in their political decisions, including voting @xcite . furthermore, @xcite explored the relationship between values and people's opinions regarding movement restrictions and social distancing measures in the context of covid-19. personality theories, which seek to comprehend human behavior and cognition, have been employed in the realm of natural language processing (nlp) research. lately, there has been an escalating interest in exploring the utilization of these theories within generative language models, with the purpose of generating sentences that closely resemble human-like language. those studies aimed to identify the mbti type and human value scale of llms by prompting them to answer questionnaires like the mbti questionnaire or portrait values questionnaire (pvq) @xcite . in addition to measuring personality, there are studies that quantitatively measured whether prompting can induce desired personality traits @xcite . researchers have explored various techniques to guide language models in generating text that reflects specific personas or styles. for example, @xcite conducted a study analyzing effective prompts in the in-context learning approach, which enables the generation of desired sentences without fine-tuning the model. in another study, @xcite demonstrated exceptional performance in tasks such as learning from intended instructions and mitigating the generation of toxic output by utilizing reinforcement learning 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. ## 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 a target value distribu- ## 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 scenarios and tasks, such as, in a value-profiling survey and argumentation. ## experiment 2: opinion & behavior predictions with value-injected llms given value-injected llms, we evaluate their ability to predict human behaviors in everyday scenarios and opinions on various issues based on the underlying value distribution. ## conclusion 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 answering tasks. to assess the effectiveness of vim across various value distributions, we conducted evaluations 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 outperforms other prompting methods in these evaluations. additionally, we examined the efficacy of value injection and its ability to predict human behavior through behavior prediction and opinion prediction tasks. the empirical experiments conducted on these evaluation tasks confirm the effectiveness of vim in value injection and its superior performance compared to other prompting methods in predicting human behaviors.