Discourse Heuristics For Paradoxically Moral Self-Correction

• URL: http://arxiv.org/abs/2507.00985v1
• Date: Tue, 01 Jul 2025 17:36:41 GMT
• Title: Discourse Heuristics For Paradoxically Moral Self-Correction
• Authors: Guangliang Liu, Zimo Qi, Xitong Zhang, Kristen Marie Johnson,
• Abstract summary: Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models with human moral values.<n>We show that moral self-correction relies on discourse constructions that reflect shortcuts.<n>We propose a solution to improve moral self-correction by leveraging the generalizations of curated datasets.
• Score: 6.360181137608509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models (LLMs) with human moral values. However, moral self-correction techniques are subject to two primary paradoxes. First, despite empirical and theoretical evidence to support the effectiveness of self-correction, this LLM capability only operates at a superficial level. Second, while LLMs possess the capability of self-diagnosing immoral aspects of their output, they struggle to identify the cause of this moral inconsistency during their self-correction process. To better understand and address these paradoxes, we analyze the discourse constructions in fine-tuning corpora designed to enhance moral self-correction, uncovering the existence of the heuristics underlying effective constructions. We demonstrate that moral self-correction relies on discourse constructions that reflect heuristic shortcuts, and that the presence of these heuristic shortcuts during self-correction leads to inconsistency when attempting to enhance both self-correction and self-diagnosis capabilities jointly. Based on our findings, we propose a solution to improve moral self-correction by leveraging the heuristics of curated datasets. We also highlight the generalization challenges of this capability, particularly in terms of learning from situated context and model scales.

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