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Probing the Moral Development of Large Language Models through Defining Issues Test

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arxiv 2309.13356 v2 pith:RWEFX5AF submitted 2023-09-23 cs.CL cs.AI

classification cs.CLcs.AI
keywords moraldevelopmentreasoningabilitybetterdefiningdilemmasgpt-4
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In this study, we measure the moral reasoning ability of LLMs using the Defining Issues Test - a psychometric instrument developed for measuring the moral development stage of a person according to the Kohlberg's Cognitive Moral Development Model. DIT uses moral dilemmas followed by a set of ethical considerations that the respondent has to judge for importance in resolving the dilemma, and then rank-order them by importance. A moral development stage score of the respondent is then computed based on the relevance rating and ranking. Our study shows that early LLMs such as GPT-3 exhibit a moral reasoning ability no better than that of a random baseline, while ChatGPT, Llama2-Chat, PaLM-2 and GPT-4 show significantly better performance on this task, comparable to adult humans. GPT-4, in fact, has the highest post-conventional moral reasoning score, equivalent to that of typical graduate school students. However, we also observe that the models do not perform consistently across all dilemmas, pointing to important gaps in their understanding and reasoning abilities.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. User Behavior Prediction as a Generic, Robust, Scalable, and Low-Cost Evaluation Strategy for Estimating Generalization in LLMs

    cs.CL 2025-06 conditional novelty 5.0 of 10

    The authors introduce an entropy-based framework that uses user behavior prediction as a measure of LLM generalization, and find GPT-4o outperforms GPT-4o-mini and Llama-3.1 on movie and music recommendation tasks.

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