Summary of Logic-lm: Empowering Large Language Models with Symbolic Solvers For Faithful Logical Reasoning, by Liangming Pan et al.
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
by Liangming Pan, Alon Albalak, Xinyi Wang, William Yang Wang
First submitted to arxiv on: 20 May 2023
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 This paper introduces Logic-LM, a novel framework that integrates Large Language Models (LLMs) with symbolic solvers to improve logical problem-solving. The method first translates natural language problems into symbolic formulations using LLMs, then employs deterministic symbolic solvers for inference. A self-refinement module utilizes error messages from the symbolic solver to revise formalizations. Logic-LM is demonstrated on five logical reasoning datasets, achieving an average performance boost of 39.2% over LLM alone and 18.4% over LLM with chain-of-thought prompting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic-LM helps computers solve complex logical problems better by combining large language models with special logic-solving tools. This makes it easier for machines to understand and reason about complex ideas like math and science. The paper shows how this works on several different problem sets, and finds that the combined approach is much better than using just one or the other. |
Keywords
» Artificial intelligence » Inference » Prompting
