Summary of Exploring the Role Of Reasoning Structures For Constructing Proofs in Multi-step Natural Language Reasoning with Large Language Models, by Zi’ou Zheng et al.
Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models
by Zi’ou Zheng, Christopher Malon, Martin Renqiang Min, Xiaodan Zhu
First submitted to arxiv on: 11 Oct 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 The paper investigates whether current state-of-the-art generalist Large Language Models (LLMs) can leverage structural information from a few examples to construct proof structures with improved explainability through in-context learning. The study focuses on two methods: structure-aware demonstration and structure-aware pruning, which are demonstrated to improve performance. A detailed analysis is provided to understand the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that large language models can learn to reason more effectively by using structural information from examples. This helps them perform better and be more explainable. The researchers tested two methods: demonstrating how to do something and pruning unnecessary parts of the model. Both methods improved performance, showing that these models can learn from a few examples. |
Keywords
» Artificial intelligence » Pruning
