用专家反馈迭代修正知识库，提升小样本场景下的生成准确性。
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack
- 多智能体强化学习框架，按文档定制化编辑知识库
- 在低资源编程与问答任务中，显著提升RAG系统性能
- 适合需要高精度知识维护的私有数据场景
大型语言模型常生成错误或过时信息，尤其在低资源环境或处理私有数据时。为解决此问题，检索增强生成（RAG）依赖外部知识库（KB），但其本身也可能存在错误。本文提出STACKFEED，一种基于反馈的结构化文本演员-评论家知识库编辑方法，采用多智能体、集中式评论家的强化学习框架，根据专家反馈迭代优化知识库。每个文档配备一个基于ReACT的演员智能体，依据特定指令执行结构化编辑。实验表明，STACKFEED显著提升了知识库质量与RAG系统的性能。我们在低资源编程问题、修改后的Python包和事实性问答任务上进行了评估。
原文摘要 · Abstract (English)
Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with FEEDback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified python packaged and factual question-answering tasks.
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