Summary of Make Llms Better Zero-shot Reasoners: Structure-orientated Autonomous Reasoning, by Pengfei He et al.
Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
by Pengfei He, Zitao Li, Yue Xing, Yaling Li, Jiliang Tang, Bolin Ding
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 addresses a limitation of current zero-shot reasoning methods with Large Language Models (LLMs), which struggle to handle multi-step reasoning questions. The authors introduce a novel structure-oriented analysis method to help LLMs better understand questions and guide their problem-solving process. This approach builds upon existing strategies like Chain-of-Thought and ReAct, leveraging probabilistic graphical models to theoretically explain its effectiveness. To further improve reliability in complex question-answering tasks, the authors propose the Structure-oriented Autonomous Reasoning Agents (SARA) system, which enforces reasoning processes through refinement techniques and incorporates external knowledge retrieval to reduce factual errors. Experimental results verify the effectiveness of SARA, which even surpasses few-shot methods in some cases. This system not only improves accuracy but also demonstrates robustness against potential attacks that corrupt the reasoning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a problem with machines that can answer questions without being trained beforehand. These machines are called Large Language Models (LLMs). They’re good at answering simple questions, but struggle when questions require multiple steps of thinking. The authors created a new way for these machines to understand questions and solve problems better. This approach helps the machines follow a structure or plan to find the answer. It’s like giving them a roadmap to help them arrive at the correct answer. The authors also tested their idea with real-world data and found that it works even better than some other methods. This new way of thinking is important because it can be used for many different tasks, from answering questions to making decisions. |
Keywords
» Artificial intelligence » Few shot » Question answering » Zero shot
