Summary of Critical Tokens Matter: Token-level Contrastive Estimation Enhances Llm’s Reasoning Capability, by Zicheng Lin et al.
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM’s Reasoning Capability
by Zicheng Lin, Tian Liang, Jiahao Xu, Qiuzhi Lin, Xing Wang, Ruilin Luo, Chufan Shi, Siheng Li, Yujiu Yang, Zhaopeng Tu
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces a novel approach for improving the accuracy of large language models (LLMs) on mathematical reasoning tasks. It identifies “critical tokens” that significantly influence incorrect outcomes, and proposes a framework for identifying and replacing these tokens. The authors demonstrate the effectiveness of their approach through extensive experiments on datasets GSM8K and MATH500, using widely used models Llama-3 and Deepseek-math. Their results show that pinpointing critical tokens can lead to significant improvements in model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by fixing mistakes they make when doing math problems. It finds special “tokens” that are important for making errors, and shows how to replace these tokens to make the computer more accurate. The researchers tested their method on two big datasets and found that it works well with different types of models. |
Keywords
» Artificial intelligence » Llama
