Large language models (LLMs) have demonstrated their potential to refine their generation based on their own feedback. However, the feedback from LLM itself is often inaccurate, thereby limiting its benefits. In this paper, we propose Study Assistant for Large LAnguage Model (SALAM), a novel framework with an auxiliary agent to assist the main LLM in learning from mistakes through interactive cooperation. In the gathering phase, the student assistant agent probes the main LLM, analyzes its errors, and collects the interaction in a mistake memory. During the examination phase, the study assistant provides guidelines by retrieving relevant cases to help the main LLM anticipate and avoid similar errors. We first investigate the effectiveness of a general study assistant and then customize it to provide LLM-specific guidance through imitation learning from successful guidance experiences. Our experiments on three LLMs using two challenging frameworks demonstrate that SALAM can significantly boost LLMs by an accuracy margin of up to 6.6 on BBH and 12.6 on BBQ.

翻译：大语言模型（LLMs）已展现出基于自身反馈优化生成结果的潜力。然而，LLM自身的反馈往往不够准确，从而限制了其效益。本文提出大语言模型学习助理（SALAM）——一种新型框架，通过引入辅助智能体，以交互式协作方式帮助主LLM从错误中学习。在收集阶段，学生助理智能体对主LLM进行探查，分析其错误，并将交互过程存入错误记忆库。在考试阶段，学习助理通过检索相关案例提供指导，帮助主LLM预判并避免类似错误。我们首先探究通用学习助理的有效性，随后通过模仿学习成功指导经验，定制化提供LLM专属指导。基于三个LLM、使用两个具有挑战性框架的实验表明，SALAM在BBH基准上可将准确率提升高达6.6个百分点，在BBQ基准上提升12.6个百分点。