Summary of Mitigating Hallucinations in Large Language Models Via Self-refinement-enhanced Knowledge Retrieval, by Mengjia Niu et al.
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
by Mengjia Niu, Hao Li, Jie Shi, Hamed Haddadi, Fan Mo
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 the potential of knowledge graphs (KGs) in augmenting large language models (LLMs), addressing the challenge of hallucination. Specifically, it investigates methods to retrieve relevant facts from KGs to improve LLM accuracy and deployment in critical domains like healthcare. The proposed approach aims to alleviate resource-intensive requirements, enabling its application in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. They’re really good at some things, but sometimes they make up information that’s not true. This is a big problem when we want to use them for important tasks like helping doctors diagnose patients. To fix this issue, researchers suggest using special databases called knowledge graphs to help the models get more accurate answers. |
Keywords
» Artificial intelligence » Hallucination
