从挫折中获取智慧：通过错误分析对齐大型语言模型
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
AI总结：
本研究提出一种基于错误分析的大型语言模型对齐技术，通过故意暴露错误内容让模型学习错误原因及规避方法，在无需外部模型或人工标注的情况下，有效提升模型生成响应的安全性并保持整体效用。
摘要：
大型语言模型（LLMs）的快速发展不仅带来了众多机遇，也带来了重大挑战。当LLMs无意中或因故意诱导而产生有害或有害内容时，这一点尤为明显。现有的对齐方法通常通过利用人类标注的、无瑕疵的指令-响应对来引导LLMs走向有利结果。相反，本研究提出了一种基于错误分析的新颖对齐技术，该技术故意将LLMs暴露于错误内容中，以学习错误的原因以及如何避免它们。在这种情况下，错误被重新利用为对齐的有价值数据，有效帮助避免产生错误响应。无需外部模型或人工标注，我们的方法利用模型内在能力辨别不良错误，并提高其生成响应的安全性。实验结果表明，我们的方法在增强模型安全性方面优于现有对齐方法，同时保持整体效用。
英文摘要：
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when LLMs inadvertently generate harmful or toxic content, either unintentionally or because of intentional inducement. Existing alignment methods usually direct LLMs toward the favorable outcomes by utilizing human-annotated, flawless instruction-response pairs. Conversely, this study proposes a novel alignment technique based on mistake analysis, which deliberately exposes LLMs to erroneous content to learn the reasons for mistakes and how to avoid them. In this case, mistakes are repurposed into valuable data for alignment, effectively helping to avoid the production of erroneous responses. Without external models or human annotations, our method leverages a model's intrinsic ability to discern undesirable mistakes and improves the safety of its generated responses. Experimental results reveal that our method outperforms existing alignment approaches in enhancing model safety while maintaining the overall utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
