The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models (2024)

Abstract: Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. These cognitive-behavioral traits include typically Attitudes, Opinions, Values (AOVs). However, measuring AOVs embedded within LLMs remains opaque, and different evaluation methods may yield different results. This has led to a lack of clarity on how different studies are related to each other and how they can be interpreted. This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs. Moreover, we survey related approaches in different stages of the evaluation pipeline in these works. By doing so, we address the potential and challenges with respect to understanding the model, human-AI alignment, and downstream application in social sciences. Finally, we provide practical insights into evaluation methods, model enhancement, and interdisciplinary collaboration, thereby contributing to the evolving landscape of evaluating AOVs in LLMs.

## introduction recent years have witnessed a remarkable improvement in the development of large language models (llms), holding the promise of boosting various domains, from computer sciences to social sciences and beyond @xcite . amid the excitement surrounding their capabilities, when we take a human-centric perspective, an important question lies: how well do these llms capture and convey human cognitive-behavioral traits? by drawing upon traditional theories from the social sciences (such as @xcite @xcite , we consider human * lead authors. in recent studies, survey questionnaires that were originally used to estimate public opinions in the social sciences are now being popularly utilized to evaluate the opinions of llms and subsequently to study the alignment with human opinions @xcite @xcite . at the same time, the wide range of evaluation methods used to assess llm responses has led to inconsistent outcomes, complicating reliable assessment of the models. for instance, @xcite demonstrate that different prompting methods lead to different results; @xcite show that output extraction methods can yield misaligned results. however, this variability in evaluation methods has sometimes been overlooked in real usecases-posing risks of missing subtleties in llm performance, yielding incomplete or biased assessments. this oversight raises questions about the model's true capabilities and its alignment with human opinions. motivated by the rising interest in studying the human-like traits of llms, in this paper, we present the first survey on the evaluation of aovs in llms. before moving into the details, we first position our survey in the context of other relevant surveys and then show the framework of our survey. ## definitions next, we provide definitions for the three main concepts used in this paper: attitude, opinion, and value (what are they?). according to @xcite , an attitude is a durable orientation toward some object, while an opinion is more of a visible expression of an attitude. for this paper, we examine the two concepts simultaneously following bergman (1998), who considers the attitude and opinion as synonymous: citation 1. "attitudes (and opinions) are always attitudes about something. this implies three necessary elements: first, there is the object of thought, which is both constructed and evaluated. second, there are acts of construction and evaluation. third, there is the agent, who is doing the constructing and evaluating. we can therefore suggest that, at its most general, an attitude is the cognitive construction and affective evaluation of an attitude object by an agent." @xcite we apply the above definition to the study of llm attitudes and opinions. these three elements are formed as follows: first, there is the topic under consideration as the object of thought; second, there are the internal mechanisms and processes within the llm that perform the construction and evaluation of this topic; and third, there is the llm itself as the agent. on value, bergman (1998)'s definition reads: citation 2. "a value may be understood as the cognitive and affective evaluation of an array of objects by a group of agents." @xcite this definition suggests that values extend beyond individual attitudes and opinions, denoting grouped thoughts and evaluations of an array of objects. llms were trained on a great amount of textual data from billions of humans. this means that when prompted, llms might sometimes generate responses that incorporate these varied perspectives rather than a single viewpoint @xcite @xcite @xcite . llms could be understood "as a superposition of perspectives" @xcite and have both dimensions. thus, in our paper, we suggest to consider the terms attitudes, opinions, and values together and to study them as a cohesive set. we propose a two-dimensional view for it: attitudes and opinions encompass the attitudes and opinions prevalent in societal contexts, often captured through timely surveys and polls; values look deeper into the ethical and cultural beliefs that guide individual and collective behavior, usually more stable over time. ## an overview of related works for aovs in llms in this section, we present related recent works on the evaluation of aovs in llms (what so far?). we categorize the works into two main groups: attitudes/opinions and values, reflecting the two dimensions of aovs we proposed. in addition, we include works with various topics that could also shine light on aovs in llms. a summary of the surveyed papers, along with the details on the paper selection and an analysis of the model distribution can be found in the appendix ( §a.1, §a.2). ## how llms are queried for aovs after defining the core concepts and discussing related works, we now provide details of the pipeline on how llms were queried for aovs so far (how?) to motivate our later discussion on gaps. based on the surveyed works, we categorize the evaluation process in a taxonomy into four main subcategories: i) input, ii) model, iii) output, and iv) evaluation, covering the four main stages of the evaluation process, as illustrated in figure 1 . ## opportunities and challenges in evaluating aovs in llms drawing from findings summarized in §3 and §4 from recent advances, we now focus on the methodological and practical perspectives regarding opportunities and challenges of evaluating aovs in llms (where?). this section addresses several key issues starting with the need to understand the models themselves (step 1), followed by the necessity for human-ai alignment (step 2), and finally, the implications from and for downstream applications in social sciences (step 3). ## towards a future of evaluating aovs in llms as we have discussed, evaluating aovs in llms offers opportunities alongside notable challenges ( §5). to harness these opportunities while addressing the challenges, we show below key areas where focused action may lead to substantial improvements (what to do?). develop a more fine-grained and human-centered evaluation pipeline. the current methods for evaluating aovs in llms within the pipeline sometimes lack the necessary rigor for robust and reliable evaluations, especially due to the unstable results from the current evaluation methods. we call for the development of a more robust and fine-grained evaluation pipeline that can better capture the nuances of human-like expressions in llm outputs. besides, there is a great gap in the evaluation benchmarks. the current existing benchmarks for evaluating the opinions in llms such as opinionqa @xcite and mmlu @xcite are static. interactive benchmarks such as alpacaeval @xcite and mt-bench @xcite focus more on general preferences. therefore, more humancentered and fine-grained benchmarks from cognitive and social sciences should also be explored and extended to validate the "human" factors within the models in real-world scenarios. ## limitations in this work, we present a survey and commentary on the progress and challenges of evaluating aovs in llms. there are several key limitations that should be acknowledged: inclusivity of related work. this survey predominantly focuses on works with subjective context related to opinions and values. as a result, other relevant areas such as emotion detection, (e.g. @xcite , which might implicitly contain value expressions, have not been included here. future research could explore a broader range of related works beyond aovs. perspective on the evaluation pipeline. the discussion on the evaluation pipeline in this work may be limited in scope, mainly focusing on the four evaluation stages, however decisions in each step have potential for profound impact on results. while we provide an overview of the evaluation pipeline with diverse approaches in each evaluation stage, there may be additional aspects or single features of the evaluation pipeline that were not thoroughly examined or highlighted, such as detailed pre-processing and data augmentation methods, intermediate representation analysis and error analysis methods. future studies could delve deeper into these aspects to contribute in providing an even more comprehensive understanding of the evaluation process of aovs in llms. exploration of use cases. this work primarily focuses on the evaluation aspect of llms and does not extensively explore their detailed use cases in social science and society. while evaluating aovs in llms is undoubtedly important, it is equally crucial to consider how these models can be applied in various domains to address real-world challenges. future research could explore the broader implications of llms in potential use cases, such as social science research, policy-making, education, and other societal applications to provide a more holistic perspective on their utility and impact. ## ethical considerations within the surveyed papers and approaches, there might exist contents that could potentially raise ethical considerations, due to the nature of the subjectivity in these topics. we report these in two key aspects: ethical considerations regarding the data used. in future studies involving the collection of new survey and questionnaire data, researchers must exercise caution and be mindful of ethical concerns, especially with regard to sensitive topics. it is crucial to design questions in a way that avoids causing direct or indirect harm to participants. ensuring ethical sensitivity in the data collection process is vital to maintaining the integrity and safety of the research @xcite . alignment studies also often require comparing llm responses with those from real human participants. researchers should ensure that these human participants provide informed consent and that their privacy is protected.