Summary of Large Language Models As An Indirect Reasoner: Contrapositive and Contradiction For Automated Reasoning, by Yanfang Zhang et al.
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning
by Yanfang Zhang, Yiliu Sun, Yibing Zhan, Dapeng Tao, Dacheng Tao, Chen Gong
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach to improve the complex reasoning capabilities of Large Language Models (LLMs). Building upon existing methods like Chain-of-Thought (CoT), the authors introduce a Direct-Indirect Reasoning (DIR) method that considers both direct and indirect reasoning paths. By crafting prompt templates incorporating logical principles, LLMs are stimulated to implement indirect reasoning, enhancing their comprehension of rules used in the reasoning process. The DIR method is simple yet effective and can be seamlessly integrated with existing CoT variants. Experimental results on four datasets demonstrate significant performance improvements when combining DIR with baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand complex ideas better. It’s like teaching a student to solve puzzles by breaking them down into smaller steps. Right now, most computer programs just take the easiest way to find an answer, but this method shows how to use both easy and harder ways to get the correct solution. The authors make it easier for computers to understand by giving them special instructions that encourage them to think in a more logical way. This new approach works really well and can be used with other existing methods. |
Keywords
» Artificial intelligence » Prompt
