让大模型自动迁移经验，无需人工干预就能提升新任务表现。
ExpeTrans: LLMs Are Experiential Transfer Learners
- 大模型自主从已有任务迁移经验到新任务
- 13个数据集上均显著提升性能
- 适合需要快速适应新任务的场景
近期研究通过提示词向大语言模型（LLMs）注入文本形式的任务求解经验以提升其表现。然而，以往方法依赖大量人力或时间收集每项任务的经验，面对用户查询中任务类型日益多样，已不具可行性。为此，我们设计了一个自主经验迁移框架，探究大模型能否模拟人类认知智能，自主将已有源任务的经验迁移到新遇到的目标任务。这不仅避免了以往方法的高成本，也为大模型泛化能力提供了新路径。在13个数据集上的实验结果表明，该框架能有效提升大模型性能。此外，我们对框架中各模块进行了详细分析。
原文摘要 · Abstract (English)
Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance. However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs. To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs. Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework.
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