Summary of Can We Verify Step by Step For Incorrect Answer Detection?, By Xin Xu et al.
Can We Verify Step by Step for Incorrect Answer Detection?
by Xin Xu, Shizhe Diao, Can Yang, Yang Wang
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper introduces a new benchmark, R2PE, designed to explore the relationship between reasoning chains and performance in various tasks. The authors aim to predict the accuracy of large language model (LLM) outputs by scrutinizing the reasoning chains they generate. To achieve this goal, the researchers propose the process discernibility score (PDS) framework, which outperforms a baseline answer-checking approach. The PDS framework leads to an average increase in F1 score and AUC-PR across 45 subsets within R2PE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can now think more like humans! This new benchmark helps us figure out if we can predict how well these AI models do by looking at the way they reason. It’s like a puzzle, where we try to see if we can tell which answer is correct just by seeing the steps the model takes to get there. |
Keywords
» Artificial intelligence » Auc » F1 score » Large language model
