Summary of Don’t Forget to Connect! Improving Rag with Graph-based Reranking, by Jialin Dong et al.
Don’t Forget to Connect! Improving RAG with Graph-based Reranking
by Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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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 This paper explores ways to improve the performance of large language models (LLMs) in generating responses by grounding generation with context from existing documents. The authors propose a new approach called G-RAG, which uses graph neural networks (GNNs) to rank retrieved documents and provide a more informed response. The method combines connections between documents and semantic information to improve the relevance of generated text. Experimental results show that G-RAG outperforms state-of-the-art approaches while having a smaller computational footprint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at answering questions by using information from other relevant texts. Right now, these models do well when the texts are directly related to the question. But what if the text has only some of the information needed, or the connection isn’t obvious? The researchers developed a new way called G-RAG that uses special computer programs (GNNs) to help decide which texts are most relevant. They tested their method and found it worked better than other approaches while using less computer power. |
Keywords
» Artificial intelligence » Grounding » Rag
