Summary of Large Language Models Are Neurosymbolic Reasoners, by Meng Fang et al.
Large Language Models Are Neurosymbolic Reasoners
by Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang
First submitted to arxiv on: 17 Jan 2024
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 In this paper, researchers explore the potential of Large Language Models (LLMs) as symbolic reasoners, particularly in text-based games that require natural language understanding. The authors propose a novel LLM agent designed to tackle symbolic challenges and achieve in-game objectives. They initialize the agent with observations from the game environments, valid actions, and a specific symbolic module. Experimental results show that their method significantly enhances the capability of LLMs for automated agents in symbolic reasoning tasks, achieving an average performance of 88% across all tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using big language models to help computers play text-based games like math problems or sorting words. The researchers created a new type of model that can understand and solve these kinds of challenges. They tested it on different types of games and found that it did really well, with an average score of 88%. |
Keywords
» Artificial intelligence » Language understanding
