Value Portrait: Assessing Language Models' Values through Psychometrically and Ecologically Valid Items

• URL: http://arxiv.org/abs/2505.01015v3
• Date: Wed, 11 Jun 2025 05:38:26 GMT
• Title: Value Portrait: Assessing Language Models' Values through Psychometrically and Ecologically Valid Items
• Authors: Jongwook Han, Dongmin Choi, Woojung Song, Eun-Ju Lee, Yohan Jo,
• Abstract summary: Existing benchmarks rely on human or machine annotations that are vulnerable to value-related biases.<n>We propose the Value Portrait benchmark, which consists of items that capture real-life user-LLM interactions.<n>This psychometrically validated approach ensures that items strongly correlated with specific values serve as reliable items for assessing those values.
• Score: 2.9357382494347264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The importance of benchmarks for assessing the values of language models has been pronounced due to the growing need of more authentic, human-aligned responses. However, existing benchmarks rely on human or machine annotations that are vulnerable to value-related biases. Furthermore, the tested scenarios often diverge from real-world contexts in which models are commonly used to generate text and express values. To address these issues, we propose the Value Portrait benchmark, a reliable framework for evaluating LLMs' value orientations with two key characteristics. First, the benchmark consists of items that capture real-life user-LLM interactions, enhancing the relevance of assessment results to real-world LLM usage. Second, each item is rated by human subjects based on its similarity to their own thoughts, and correlations between these ratings and the subjects' actual value scores are derived. This psychometrically validated approach ensures that items strongly correlated with specific values serve as reliable items for assessing those values. Through evaluating 44 LLMs with our benchmark, we find that these models prioritize Benevolence, Security, and Self-Direction values while placing less emphasis on Tradition, Power, and Achievement values. Also, our analysis reveals biases in how LLMs perceive various demographic groups, deviating from real human data.

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