ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models
This addresses the need for safer and more ethical LLMs by improving their moral decision-making, though it is incremental as it builds on existing contrastive learning and reasoning techniques.
The paper tackles the problem of making moral judgments by Large Language Models (LLMs) more trustworthy and explainable by introducing ClarityEthic, a method that uses contrastive learning to identify relevant social norms, which outperforms state-of-the-art approaches in accuracy and provides plausible explanations.
With the rise and widespread use of Large Language Models (LLMs), ensuring their safety is crucial to prevent harm to humans and promote ethical behaviors. However, directly assessing value valence (i.e., support or oppose) by leveraging large-scale data training is untrustworthy and inexplainable. We assume that emulating humans to rely on social norms to make moral decisions can help LLMs understand and predict moral judgment. However, capturing human values remains a challenge, as multiple related norms might conflict in specific contexts. Consider norms that are upheld by the majority and promote the well-being of society are more likely to be accepted and widely adopted (e.g., "don't cheat,"). Therefore, it is essential for LLM to identify the appropriate norms for a given scenario before making moral decisions. To this end, we introduce a novel moral judgment approach called \textit{ClarityEthic} that leverages LLMs' reasoning ability and contrastive learning to uncover relevant social norms for human actions from different perspectives and select the most reliable one to enhance judgment accuracy. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in moral judgment tasks. Moreover, human evaluations confirm that the generated social norms provide plausible explanations that support the judgments. This suggests that modeling human moral judgment with the emulating humans moral strategy is promising for improving the ethical behaviors of LLMs.