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Jun, 2024
多头RAG:使用LLMs解决多方面问题
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
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Maciej Besta, Ales Kubicek, Roman Niggli, Robert Gerstenberger, Lucas Weitzendorf...
TL;DR
通过利用Transformer的多头attention层的激活作为提取多方面文档的关键来提高文本生成模型的能力,使得Multi-Head RAG能够更准确地检索复杂查询,并通过实证评估显示在相关性方面相较于标准的RAG基准模型有着高达20%的改进。
Abstract
retrieval augmented generation
(RAG) enhances the abilities of
large language models
(LLMs) by enabling the retrieval of documents into the LLM context to provide more accurate and relevant responses. Existing RA
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