An Empirical Study of Counterfactual Self-Explanations in LLMs

2026-09-15

AI Summary

研究通过反事实自我解释评估大语言模型解释自身行为的准确性，使用最小编辑输入改变预测的方法，发现模型规模是解释质量的主要决定因素。

Abstract

Large language models can easily generate explanations for their own outputs, but such self-explanations are not necessarily faithful to the model's behavior. We study this issue through counterfactual self-explanations, where a model minimally edits an input so that its own prediction changes. Across sentiment analysis and natural language inference, we evaluate ten instruction-tuned models from the LLaMA-3 and Qwen-2.5 families, measuring faithfulness, minimality, and alignment with human-annotated rationales. Our results show that model scale is the strongest determinant of explanation quality: larger models are substantially more likely to generate counterfactuals that flip their own predictions and target decision-relevant evidence. In contrast, the rationale-guided condition produces edit-minimal counterfactuals that are also more human-aligned. However, it does not consistently improve faithfulness. Overall, counterfactual self-explanations can provide useful behavioral evidence about model decisions, but their reliability depends strongly on model capacity and should be empirically validated rather than assumed.

Problem

• counterfactual self-explanations
• large language models
• faithfulness

Innovation

• counterfactual self-explanations
• model scale
• faithfulness
• rationale-guided