Summary of Self-consistency Boosts Calibration For Math Reasoning, by Ante Wang et al.
Self-Consistency Boosts Calibration for Math Reasoning
by Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu
First submitted to arxiv on: 14 Mar 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 This paper proposes three novel calibration methods for large language models (LLMs) to improve their math reasoning capabilities. The proposed methods are based on self-consistency, a concept introduced by Wang et al. in 2022. These methods are evaluated on two popular benchmarks: GSM8K and MathQA. The results show that the new approaches outperform existing calibration techniques, including those using p(True) or logit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) need to be calibrated so they can accurately predict their confidence levels. This paper shows how three simple methods can help LLMs get better at math reasoning tasks. The results are impressive, and the new approaches work well on two popular tests: GSM8K and MathQA. |
