Summary of G-retriever: Retrieval-augmented Generation For Textual Graph Understanding and Question Answering, by Xiaoxin He et al.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
by Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel framework for answering questions about textual graphs, enabling users to “chat with their graph” using a conversational interface. The method provides textual replies and highlights relevant parts of the graph, outperforming baselines on multiple domains and mitigating hallucination. The framework, called G-Retriever, uses a retrieval-augmented generation approach to answer questions about general textual graphs, formulating the task as a Prize-Collecting Steiner Tree optimization problem to resist hallucination and handle large graph sizes. The paper also introduces a Graph Question Answering (GraphQA) benchmark with data collected from different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big database of information that’s organized like a graph, where each piece of information is connected to other related pieces. Now imagine being able to ask questions about this graph using a conversation, and getting answers back in plain language. That’s what this paper makes possible. The researchers developed a new way to answer questions about graphs, which can be used for things like understanding scenes, common sense reasoning, and more. They tested their method on several tasks and showed that it works well, even with very large amounts of data. |
Keywords
* Artificial intelligence * Hallucination * Optimization * Question answering * Retrieval augmented generation
