Summary of Improving Arithmetic Reasoning Ability Of Large Language Models Through Relation Tuples, Verification and Dynamic Feedback, by Zhongtao Miao et al.
Improving Arithmetic Reasoning Ability of Large Language Models through Relation Tuples, Verification and Dynamic Feedback
by Zhongtao Miao, Kaiyan Zhao, Yoshimasa Tsuruoka
First submitted to arxiv on: 25 Jun 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 proposed approach introduces a novel representation for reasoning steps in large language models, moving away from natural language and programming code. The semi-structured form, based on relation tuples, is designed to be both human-readable and machine-friendly. A framework comprising three components is implemented: introducing relation tuples into the reasoning process, automatic verification using a local code interpreter, and integrating a dynamic feedback mechanism for self-improvement. Experimental results demonstrate improved arithmetic reasoning abilities in large language models on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models use two main types of representations: natural language and programming code. These can be difficult to understand or verify. A new way to represent reasoning steps is proposed, using semi-structured relation tuples that are easy for people and machines to read. The approach includes introducing these tuples into the reasoning process, verifying them with a local code interpreter, and providing feedback to help the model improve itself. This method improves the arithmetic abilities of large language models on different datasets. |
