Summary of Re-ex: Revising After Explanation Reduces the Factual Errors in Llm Responses, by Juyeon Kim et al.
Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses
by Juyeon Kim, Jeongeun Lee, Yoonho Chang, Chanyeol Choi, Junseong Kim, Jy-yong Sohn
First submitted to arxiv on: 27 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 In this paper, researchers tackle the challenge of deploying large language models (LLMs) in real-world scenarios by addressing hallucination issues. They propose a method called Re-Ex for post-editing LLM-generated responses, which involves three steps: retrieving evidence for factual errors, having the LLM explain problematic parts based on that evidence, and then revising the initial response using those explanations. The authors also introduce new prompting techniques to reduce the token count and inference time required for response revision. Compared to existing methods like FacTool, CoVE, and RARR, Re-Ex demonstrates better detection and revision performance with reduced inference time and fewer tokens across multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Re-Ex is a way to make sure language models don’t make up fake information. It’s like having a fact-checker that helps the model correct its mistakes. This method has three parts: find evidence for what’s wrong, ask the model to explain why it made a mistake, and then fix the mistake based on that explanation. The authors also came up with new ways to help the model revise its responses quickly and efficiently. Overall, Re-Ex is better than other methods at finding and fixing errors while using fewer resources. |
Keywords
» Artificial intelligence » Hallucination » Inference » Prompting » Token
