Summary of Towards Generalizable and Faithful Logic Reasoning Over Natural Language Via Resolution Refutation, by Zhouhao Sun et al.
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
by Zhouhao Sun, Xiao Ding, Li Du, Bibo Cai, Jinglong Gao, Ting Liu, Qin Bing
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposes a novel framework called Generalizable and Faithful Reasoner (GFaiR) to improve the performance of large language models (LLMs) in first-order logic reasoning tasks. The existing LLMs-based reasoning systems suffer from theoretical incompleteness, which limits their ability to generalize and address complex scenarios. To overcome this issue, GFaiR introduces the paradigm of resolution refutation, which enables the system to solve all first-order logic reasoning problems by extending reasoning rules and employing proof by contradiction. Experimental results show that GFaiR outperforms previous works in complex scenarios while maintaining performance in simple scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made significant progress in natural language tasks, but they struggle with formal logical theories expressed in natural language. This is because existing systems are incomplete and can only solve simple problems, making them not very good at generalizing to new situations. To help with this, the authors suggest a new way of doing things called GFaiR (Generalizable and Faithful Reasoner). This method uses something called resolution refutation, which helps the system figure out answers by looking at contradictions. The results show that their approach does better than other methods in hard cases while still getting the easy ones right. |
