Summary of Multi-head Rag: Solving Multi-aspect Problems with Llms, by Maciej Besta et al.
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
by 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
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Multi-Head Retrieval Augmented Generation (MRAG), a novel scheme that 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. MRAG addresses the challenge of fetching multiple documents with substantially different contents, which occur frequently but are hard to retrieve due to distant embeddings in the embedding space. The paper leverages activations from Transformer’s multi-head attention layer as keys for fetching multi-aspect documents, harnessing different facets of data items and queries. This leads to improved retrieval accuracy for complex queries, demonstrated by up to 20% improvements over standard RAG baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make language models better at answering questions by bringing in more relevant information from other sources. It’s like a superpower for AI that can help answer tricky questions. The new method is called Multi-Head Retrieval Augmented Generation, and it works by looking at how different parts of the model pay attention to different pieces of information. This helps bring back more accurate and relevant answers. |
Keywords
* Artificial intelligence * Attention * Embedding space * Multi head attention * Rag * Retrieval augmented generation * Transformer
