Summary of Gem-rag: Graphical Eigen Memories For Retrieval Augmented Generation, by Brendan Hogan Rappazzo et al.
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation
by Brendan Hogan Rappazzo, Yingheng Wang, Aaron Ferber, Carla Gomes
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract proposes advancements in Retrieval Augmented Generation (RAG) for Large Language Models (LLMs). The goal is to enable LLMs to optimally encode, store, and retrieve memories, which would unlock their full potential as AI agents. The method, Graphical Eigen Memories For Retrieval Augmented Generation (GEM-RAG), generates “utility” questions for each text chunk, connects chunks based on text and question similarity, and uses eigendecomposition to build higher-level summary nodes. Evaluations show that GEM-RAG outperforms state-of-the-art RAG methods on two standard QA tasks using LLMs such as UnifiedQA and GPT-3.5 Turbo with SBERT and OpenAI’s text encoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GEM-RAG is a new way to help Large Language Models remember things better. Right now, these models can learn from examples, but they have trouble remembering what they’ve learned. To fix this, researchers came up with an idea called Retrieval Augmented Generation (RAG). RAG helps models by giving them more information to work with. In this case, the new method, GEM-RAG, is designed to help models remember main ideas and themes from text. It does this by asking itself questions about each piece of text and connecting similar pieces together. The results show that GEM-RAG works better than other methods on certain tasks. |
Keywords
» Artificial intelligence » Gpt » Rag » Retrieval augmented generation
