Summary of Processbench: Identifying Process Errors in Mathematical Reasoning, by Chujie Zheng et al.
ProcessBench: Identifying Process Errors in Mathematical Reasoning
by Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
First submitted to arxiv on: 9 Dec 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 As language models often struggle to solve math problems correctly, identifying errors in their reasoning process becomes crucial for ensuring their scalability. This paper introduces ProcessBench, a benchmark designed to measure a model’s ability to identify erroneous steps in mathematical reasoning. The dataset consists of 3,400 test cases focused on competition- and Olympiad-level math problems. Each test case contains a step-by-step solution with error location annotated by human experts. Models must identify the earliest step containing an error or conclude that all steps are correct. We evaluate ProcessBench extensively, using two types of models: process reward models (PRMs) and critic models. Our results show that existing PRMs typically fail to generalize to more challenging math problems beyond GSM8K and MATH, underperforming critic models and our own trained PRM. Notably, the best open-source model, QwQ-32B-Preview, demonstrates critique capabilities competitive with the proprietary GPT-4o, despite lagging behind reasoning-specialized o1-mini. We hope ProcessBench will foster future research in reasoning process assessment, paving the way for scalable oversight of language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated math problem-solving is getting better, but sometimes they make mistakes. To help fix this, researchers created a special test to see if AI can find where it goes wrong. This test has 3,400 problems that are really hard and require human experts to check the answers. The goal is to identify the first step where the AI gets an answer wrong or prove that all steps are correct. They tested two types of AI models: ones that get rewarded for good behavior and ones that give feedback to improve. The results show that most AI models can’t solve harder math problems, but some are better than others at giving feedback. This new test will help researchers make better AI models that can catch their own mistakes. |
Keywords
» Artificial intelligence » Gpt
