Summary of The Role Of Deductive and Inductive Reasoning in Large Language Models, by Chengkun Cai et al.
The Role of Deductive and Inductive Reasoning in Large Language Models
by Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Serge Belongie, Lei Li
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Large Language Models (LLMs) have been impressive in reasoning tasks, but they’re limited by static prompt structures and can’t adapt well to complex scenarios. Our new framework, DID (Deductive and InDuctive), combines deductive and inductive reasoning approaches to enhance LLM capabilities. It uses cognitive science principles to evaluate task difficulty and guide decomposition strategies. This enables the model to adapt its reasoning pathways based on problem complexity, mirroring human cognition. We tested DID across multiple benchmarks, including AIW, MR-GSM8K, and our custom Holiday Puzzle dataset for temporal reasoning. The results show significant improvements in reasoning quality and solution accuracy, with 70.3% accuracy on AIW (compared to 62.2% for Tree of Thought) while maintaining lower computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many things well, but they need help to reason about complex problems. Our new idea is called DID, which is a combination of two ways that humans think: deductive and inductive reasoning. We used ideas from cognitive science to make this work better. This helps the model figure out how hard a problem is and then break it down into smaller parts. This makes the model better at solving problems by itself. We tested DID on many different types of problems, and it did really well! It was able to solve problems that other models couldn’t, and it didn’t use as much computer power. |
Keywords
» Artificial intelligence » Prompt
