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

Created by
  • Haebom

Author

Maciej Besta, Ales Kubicek, Robert Gerstenberger, Marcin Chrapek, Roman Niggli, Patrik Okanovic, Yi Zhu, Patrick Iff, Michal Podstawski, Lucas Weitzendorf, Mingyuan Chi, Joanna Gajda, Piotr Nyczyk, J urgen M uller, Hubert Niewiadomski, Torsten Hoefler

Outline

In this paper, we present a novel Retrieval Augmented Generation (RAG) technique called Multi-Head RAG (MRAG) to improve the response to complex queries involving multiple aspects. Conventional RAGs lack the ability to process complex queries that retrieve multiple documents with different contents. MRAG proposes a method to retrieve documents with multiple aspects by leveraging the activations of the multi-head attention layer of the Transformer. Based on the fact that each attention head captures a different aspect of the data, we generate embeddings representing different aspects by utilizing the activations, thereby improving the retrieval accuracy for complex queries. Experimental results show that MRAG improves the retrieval success rate by up to 20% compared to 18 conventional RAG techniques, and also contributes to improving the performance of LLM generation.

Takeaways, Limitations

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Takeaways:
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We present a novel RAG technique (MRAG) that enables multi-faceted document retrieval by leveraging Transformer's multi-head attention mechanism.
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Solving complex query processing problems with multiple aspects of existing RAG's Limitations.
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See up to 20% improvement in search success rate and improved LLM creation performance.
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Possibility of seamless integration with existing RAG frameworks and benchmarks.
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Performance validation with multi-faceted datasets and real-world use cases.
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Limitations:
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Further verification of the generalizability of the experimental results presented in the paper is needed.
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There may be bias in performance towards certain types of queries or datasets.
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Further analysis of the computational cost and efficiency of MRAG is needed.
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