Summary of Can Language Models Pretend Solvers? Logic Code Simulation with Llms, by Minyu Chen et al.
Can Language Models Pretend Solvers? Logic Code Simulation with LLMs
by Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu, Yuxin Su, Xi Chang, Jianxin Xue
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO); Software Engineering (cs.SE)
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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 Recent research on large language models (LLMs) has focused on their capabilities in addressing logic problems. Leveraging the strengths of LLMs for code-related activities, several frameworks have been proposed to utilize logical solvers for logic reasoning. However, existing work primarily views LLMs as natural language logic translators or solvers, rather than logic code interpreters and executors. This study explores a novel aspect: logic code simulation, which requires LLMs to emulate logical solvers in predicting the results of logical programs. The research formulates three questions: Can LLMs efficiently simulate logic code outputs? What strengths arise from logic code simulation? And what pitfalls exist? To investigate these queries, three datasets were curated for the logic code simulation task, and thorough experiments were conducted to establish a baseline performance for LLMs in code simulation. A pioneering LLM-based code simulation technique, Dual Chains of Logic (DCoL), was introduced, which demonstrated state-of-the-art performance compared to other LLM prompt strategies, achieving a notable improvement in accuracy by 7.06% with GPT-4-Turbo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how large language models can be used for logic code simulation. Logic code simulation requires the model to predict the output of a logical program. The researchers ask three questions: Can LLMs do this task efficiently? What are the benefits and drawbacks of using LLMs in this way? They created special datasets and tested different approaches to find out how well LLMs can perform this task. One approach, called Dual Chains of Logic (DCoL), did surprisingly well, beating other methods by a significant margin. |
Keywords
* Artificial intelligence * Gpt * Prompt
