Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeating similar mistakes. In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes. Considering data arrives sequentially, LLMs gradually accumulate rules from incorrect cases, forming a rule collection. These rules are then utilized by the LLMs to avoid making similar mistakes when processing subsequent inputs. Moreover, the rules remain independent of the primary prompts, seamlessly complementing prompt design strategies. Experimentally, we show that TRAN improves over recent baselines by a large margin.

翻译：大语言模型（LLMs）已展现出令人瞩目的性能。然而，由于无法捕捉样本间的关联，这些冻结参数的LLMs会不可避免地重复犯类似错误。本研究提出免调优规则积累（TRAN）框架，引导LLMs通过从先前错误中学习来提升性能。考虑到数据按序到达的特点，LLMs逐步从错误案例中积累规则，形成规则集合。处理后续输入时，这些规则被LLMs用于避免犯类似错误。此外，规则与主提示保持独立，能无缝补充提示设计策略。实验表明，TRAN在近期基线方法上取得了显著改进。