让大模型多想几种解释，提升推理鲁棒性
Exploring Explanations Improves the Robustness of In-Context Learning
- 给每个可能答案生成解释，全面探索推理路径
- 在多个数据集上显著提升分布外数据的准确率
- 适合需要可靠推理的场景，如医疗、法律应用
上下文学习（ICL）已成为利用大语言模型（LLMs）的有效范式，但其泛化能力常受限于提供示例的分布。近期通过引入解释（X-ICL）的方法提升了预测可靠性，使模型能理解并阐述正确标签背后的推理过程。本文在此基础上提出增强框架X$^2$-ICL，系统地为所有可能标签生成解释，实现更全面、更稳健的决策。在多个自然语言理解数据集上的实验验证了该方法的有效性，相比现有ICL方法，在分布外数据上表现出显著更强的鲁棒性。
原文摘要 · Abstract (English)
In-context learning (ICL) has emerged as a successful paradigm for leveraging large language models (LLMs). However, it often struggles to generalize beyond the distribution of the provided demonstrations. A recent advancement in enhancing robustness is ICL with explanations (X-ICL), which improves prediction reliability by guiding LLMs to understand and articulate the reasoning behind correct labels. Building on this approach, we introduce an advanced framework that extends X-ICL by systematically exploring explanations for all possible labels (X$^2$-ICL), thereby enabling more comprehensive and robust decision-making. Experimental results on multiple natural language understanding datasets validate the effectiveness of X$^2$-ICL, demonstrating significantly improved robustness to out-of-distribution data compared to the existing ICL approaches.
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