Summary of Logic Contrastive Reasoning with Lightweight Large Language Model For Math Word Problems, by Ding Kai et al.
Logic Contrastive Reasoning with Lightweight Large Language Model for Math Word Problems
by Ding Kai, Ma Zhenguo, Yan Xiaoran
First submitted to arxiv on: 29 Aug 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 study aims to enhance the performance of lightweight Large Language Models (LLLMs) in mathematical reasoning tasks by introducing a novel method for measuring mathematical logic similarity. The authors design an automatic screening mechanism to construct reference problems that integrate semantic and logical similarity, and employ positive and negative example prompts to guide the model towards sound reasoning logic. This is the first attempt to utilize retrieval-enhanced generation for mathematical problem-solving. Experimental results show significant improvements over the Chain of Thought approach on two datasets: SVAMP (15.8%) and GSM8K (21.5%). When applied to a larger-scale model with 175 billion parameters, performance is comparable to the best results on both datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make computers better at math by improving how they reason and solve problems. The researchers created a new way to measure how well a computer understands math concepts and designed a system that teaches the computer to use logical thinking. They tested their method on two sets of math problems and found that it works much better than previous approaches. This could lead to computers being more helpful in education and other areas. |
