Summary of Cross-refine: Improving Natural Language Explanation Generation by Learning in Tandem, By Qianli Wang et al.
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
by Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Sebastian Möller, Vera Schmitt
First submitted to arxiv on: 11 Sep 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 The proposed Cross-Refine method employs two large language models (LLMs) as generator and critic to generate natural language explanations (NLEs). The generator produces an initial NLE, which is then refined using feedback from the critic. This approach does not require any supervised training data or additional training. The paper validates Cross-Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation, demonstrating its effectiveness in outperforming a baseline method called Self-Refine. Additionally, the study highlights the importance of feedback and suggestions in refining explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to generate natural language explanations for large language models’ decisions. It uses two models working together to make better explanations. The first model makes an initial explanation, and then the second model helps refine it. This approach doesn’t need any extra training data or special instructions. The paper tests this method on three tasks using three different language models and shows that it works better than a previous method called Self-Refine. |
Keywords
» Artificial intelligence » Nlp » Supervised
