Summary of Causejudger: Identifying the Cause with Llms For Abductive Logical Reasoning, by Jinwei He and Feng Lu
CauseJudger: Identifying the Cause with LLMs for Abductive Logical Reasoning
by Jinwei He, Feng Lu
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 propose a new framework called CauseJudger (CJ) for large language models (LLMs) to perform abductive logical reasoning. The goal is to identify the authenticity of possible causes by transforming thinking from reverse to forward and removing irrelevant information. To evaluate CJ’s effectiveness, the authors construct an abductive logical reasoning dataset called CauseLogics, containing 200,000 tasks of varying reasoning lengths. Experimental results show that CJ outperforms Zero-Shot-CoT, achieving a maximum correctness improvement of 41% when using gpt-3.5 and exceeding 90% accuracy with gpt-4 across all datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Abductive logical reasoning is like solving puzzles! Researchers have been trying to teach computers to do this too, but it’s tricky because they need to figure out what’s the real reason behind something. They propose a new way called CauseJudger (CJ) that helps large language models do this better. To test CJ, they created a big dataset of 200,000 puzzles and showed that it works really well. |
Keywords
» Artificial intelligence » Gpt » Zero shot
