Summary of Knowledge Graph-enhanced Large Language Models Via Path Selection, by Haochen Liu et al.
Knowledge Graph-Enhanced Large Language Models via Path Selection
by Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li
First submitted to arxiv on: 19 Jun 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 This paper tackles a significant issue in Large Language Models (LLMs): generating factually inaccurate outputs, known as the hallucination problem. Existing approaches rely on LLMs to extract knowledge from Knowledge Graphs (KGs), but this has limitations, such as only considering binary judgments and direct semantic relationships. To overcome these challenges, the authors propose a novel framework called KELP, which consists of three stages: generating scores for knowledge paths via latent semantic matching, selecting paths with indirect semantic relationships using trained encoding, and leveraging KG information to improve factual accuracy. The paper validates the effectiveness of KELP on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make Large Language Models (LLMs) better by making them less likely to say things that aren’t true. Right now, LLMs are great at doing many things, but they sometimes get facts wrong. To fix this, some people try to teach the LLMs more about what’s true and what’s not by using special databases called Knowledge Graphs (KGs). But there are problems with this approach. It’s like trying to use a map to find your way, but only being able to look at a tiny part of it at a time. This paper proposes a new way to do things that lets the LLMs consider more information and make better decisions. They call their new method KELP, and they tested it on real-life datasets to see if it works. |
Keywords
* Artificial intelligence * Hallucination
