V alue B ench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024)

Abstract: Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies. This development underscores the urgent need for evaluating value orientations and understanding of LLMs to ensure their responsible integration into public-facing applications. This work introduces ValueBench, the first comprehensive psychometric benchmark for evaluating value orientations and understanding in LLMs. ValueBench collects data from 44 established psychometric inventories, encompassing 453 multifaceted value dimensions. We propose an evaluation pipeline grounded in realistic human-AI interactions to probe value orientations, along with novel tasks for evaluating value understanding in an open-ended value space. With extensive experiments conducted on six representative LLMs, we unveil their shared and distinctive value orientations and exhibit their ability to approximate expert conclusions in value-related extraction and generation tasks.

## introduction large language models (llms) are transforming natural language processing (nlp) through their capability to generate knowledge-intensive and human-like text in a zero-shot manner @xcite . they are increasingly integrated into diverse human-ai systems, including critical domains such as education @xcite and healthcare @xcite , potentially influencing human decisions and cognition @xcite . the growing influence of llms raises alarm about their potential misalignment with human values @xcite . human values represent desired end states or behaviors that transcend specific situations and are pivotal in shaping both individual and collective human decision-making @xcite . they are widely recognized as a fundamental component in the study of human behavior across scientific disciplines, including psychology @xcite , sociology @xcite , and anthropology @xcite . this shared perspective leads to extensive research interest in evaluating the value orientations and value understanding in llms. an emerging body of research applies psychological theories and instruments to evaluate the value orientations of llms. these works probe llms' value orientations with psychometric inventories, mainly focusing on limited facets of personality. they employ inventories in their original questionnaire-based format and test llms with multiple-choice question answering @xcite @xcite @xcite @xcite . however, there is no evident correlation between llm responses in such controlled settings (a rating of agreement with a statement) and in authentic human-ai interactions (responses to value-related user questions), which undermines the reliability of the evaluation results. in addition, evaluating value understanding in llms is fundamental for enhancing the interpretability of their outputs and aligning their generation with human values @xcite . this line of work is constrained by limited predefined value space @xcite , heuristically generated ground truth @xcite , and oversight of the complex structure in a broad and hierarchical value space. valueeval @xcite ) -54 ✓ psychobench @xcite 13 69 ✓ valuebench (ours) 44 453 ✓ ✓ psychometric evaluations of llms. the rise of llms necessitates their comprehensive and reliable evaluations @xcite . the increasing utilization of llms as human proxies @xcite @xcite @xcite raises scientific needs to evaluate their humanoid traits @xcite @xcite @xcite . to this end, an emerging body of research, summarized in value understanding in llms. evaluating the understanding of values in llms establishes the groundwork for aligning their generation with human values @xcite . a proper value understanding in llms also qualifies them as zero-shot annotators and generators in human-level nlp tasks @xcite and, more broadly, computational social science @xcite . to this end, @xcite develop the value understanding measurement (vum) framework to quantitatively evaluate dual-level value understanding in llms. @xcite and @xcite demonstrate that the zero-shot performance of llms is close to the pretrained state-of-the-art or human annotators in assessing personality traits and human values. @xcite present valueeval, a benchmark pairing arguments with the values mostly drawn from @xcite . other efforts explore eliciting certain values and personal traits via prompt engineering @xcite @xcite . valuebench contributes to this line of work by presenting a comprehensive set of human values, an expert-annotated dataset of item-value pairs, a novel task for assessing value substructures, and evaluation pipelines in an open-ended value space. ## valuebench what values do llms portray via their generated answers? can llms understand the values behind linguistic expressions? in response to these questions, we propose valuebench, a comprehensive benchmark for evaluating value orientations and understanding. we begin by clarifying the inherent characteristics of human values. then we introduce the procedure of collecting and processing value-related psychometric materials. ## evaluations with valuebench this section presents our experimental setup, evaluation pipelines, and evaluation results. it also includes discussions of the limitations and insights drawn from both our evaluations and those commonly conducted in the field, shedding light on future research directions. in this work, we evaluate the following six llms: gpt-3.5 turbo (openai, 2023a), gpt-4 turbo (openai, 2023b), llama-2 7b @xcite , llama-2 70b @xcite , mistral 7b @xcite , and mixtral 8x7b @xcite . these llms are deliberately chosen from three series, encompassing the most popular options in both open-source and closedsource models, with each series featuring two llms of different scales. notably, both the gpt series and the llama-2 series incorporate an rlhf stage in their training procedures, while the mistral series is trained without rlhf techniques. nevertheless, all models have been trained with supervised fine-tuning (sft) to align their behaviors with ethical standards and social norms in the human-written instructions. for all models, we set the temperature to 0 or apply the greedy decoding mood. therefore, all results are deterministic. all prompts are collected in appendix b. ## conclusion this work presents valuebench, a comprehensive benchmark for evaluating value orientations and understanding in llms. valuebench comprises hundreds of multifaceted values and thousands of labeled linguistic expressions, spanning four categories in value-related psychometric inventories. we introduce novel evaluation pipelines for both value orientation and value understanding tasks, based on authentic human-ai interaction scenarios and well-established theoretical structure of the value space. evaluations of six llms unveil their shared and unique value orientations. we illustrate the capabilities and limitations of llms in value understanding, and propose effective prompting strategies to tackle associated nlp tasks within an expansive and hierarchical value space. llms demonstrate their ability to approximate expert conclusions established in psychology research. we hope that valuebench will inspire future research on psychometric evaluations and value alignment of llms. by revealing the promising capabilities of llms in value-related tasks, we aim to establish a broad foundation for interdisciplinary research in ai and psychology.