Summary of Autopsv: Automated Process-supervised Verifier, by Jianqiao Lu et al.
AutoPSV: Automated Process-Supervised Verifier
by Jianqiao Lu, Zhiyang Dou, Hongru Wang, Zeyu Cao, Jianbo Dai, Yingjia Wan, Zhijiang Guo
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposes AutoPSV, a novel method to enhance the reasoning capabilities of large language models (LLMs) by automatically annotating their reasoning steps. AutoPSV trains a verification model on the correctness of final answers, generating automatic process annotations with confidence scores indicating the probability of arriving at the correct answer from each step onward. The approach detects relative changes in confidence scores to annotate the reasoning process, allowing error detection even without ground truth answers. Experimental results validate that the verification model improves performance in selecting correct answers from multiple LLM-generated outputs across five datasets in mathematics and commonsense reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at thinking by themselves. They create a new way to check if an answer is right or wrong, called AutoPSV. This method looks at how confident the computer is about each step it takes to get to the final answer. It can even find errors without knowing what the correct answer should be! The team tested this idea on five different types of problems and found that it works really well. |
Keywords
» Artificial intelligence » Probability
