Summary of Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs For Open-ended Question Answering, by Jiaxiang Liu et al.
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering
by Jiaxiang Liu, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao
First submitted to arxiv on: 15 Feb 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 paper addresses the crucial task of mitigating hallucinations in Large Language Models (LLMs). Current methods, such as self-enhancement techniques, struggle to effectively address unknown factual hallucinations. Knowledge Graph (KG) enhancement approaches also fail to generalize across different KG sources and enhance open-ended answer questions simultaneously. To overcome these limitations, the authors propose a framework that combines Pseudo-Graph Generation and Atomic Knowledge Verification (PG&AKV). This framework leverages pseudo-graph generation for related knowledge frameworks and atomic-level knowledge querying and verification for generalizability under different KG sources. The results show a minimum improvement of 11.5 in ROUGE-L score for open-ended questions and 7.5% accuracy improvement for precise-answered questions. Additionally, the approach exhibits generalizability across different KG sources, achieving at least 3.5% performance improvement when using KGs different from the question sources. The paper’s results pave the way for enhancing LLMs by incorporating pseudo- and multisource-KGs, particularly in open-ended questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing a problem with large language models that sometimes make things up. Right now, there are ways to try to fix this, but they don’t work very well. The authors came up with a new way to do it by combining two techniques: generating fake knowledge graphs and verifying the information in those graphs. They tested their method on different kinds of questions and found that it worked much better than other methods. It even works when using different sources of knowledge, which is important for getting answers right. The results show that this new way of fixing language models could be very useful. |
Keywords
» Artificial intelligence » Knowledge graph » Rouge
