Summary of Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision, by Zihan Wang et al.
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
by Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 proposed Model-induced Process Supervision (MiPS) method automates data curation for multi-step problem solving by leveraging a reasoner and a trained verifier. MiPS samples completions of intermediate solutions through the reasoning model, defining accuracy as the proportion of correct completions. The approach improves the performance of PaLM 2 on math and coding tasks, achieving higher accuracy on GSM8K (+0.67%), MATH (+4.16%), and MBPP (+0.92%) compared to an output supervision trained verifier. Additionally, the study shows strong generalization ability across different reasoning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make computers better at solving problems that take multiple steps. They use a special kind of training data called “MiPS” that helps the computer learn from its own mistakes. This makes the computer more accurate and able to solve problems faster. The results are really good, with improvements on math and coding tasks compared to other methods. |
Keywords
* Artificial intelligence * Generalization * Palm
