Papers › Multi-Head RAG: Solving Multi-Aspect Problems with LLMs

Multi-Head RAG: Solving Multi-Aspect Problems with LLMs

7 Jun 2024arXiv:2406.05085archive 2025-07-28

Maciej Besta, Ales Kubicek, Roman Niggli, Robert Gerstenberger, Lucas Weitzendorf, Mingyuan Chi, Patrick Iff, Joanna Gajda, Piotr Nyczyk, Jürgen Müller, Hubert Niewiadomski, Marcin Chrapek, Michał Podstawski, Torsten Hoefler

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 RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents. Such queries occur frequently, but are challenging because the embeddings of these documents may be distant in the embedding space, making it hard to retrieve them all. This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea: leveraging activations of Transformer's multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. The driving motivation is that different attention heads can learn to capture different data aspects. Harnessing the corresponding activations results in embeddings that represent various facets of data items and queries, improving the retrieval accuracy for complex queries. We provide an evaluation methodology and metrics, multi-aspect datasets that we release online, and real-world use cases to demonstrate MRAG's effectiveness, showing improvements of up to 20% in relevance over standard RAG baselines. MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores.

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spcl/mrag officialmentioned in papermentioned on GitHub report

vividwalker/Multi-Head-Rag mentioned on GitHubpytorch report

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Tasks

Benchmarking Decoder RAG Retrieval Retrieval-augmented Generation

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Methods

Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Focus Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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