Summary of Fact-level Confidence Calibration and Self-correction, by Yige Yuan et al.
Fact-Level Confidence Calibration and Self-Correction
by Yige Yuan, Bingbing Xu, Hexiang Tan, Fei Sun, Teng Xiao, Wei Li, Huawei Shen, Xueqi Cheng
First submitted to arxiv on: 20 Nov 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 addresses the challenge of confidence calibration in Large Language Models (LLMs), which enables them to self-evaluate their outputs. Current methods estimate two scalars for overall response confidence and correctness, but this is insufficient for long-form generation tasks that involve multiple facts with varying levels of confidence and relevance. The proposed Fact-Level Calibration framework calibrates confidence to relevance-weighted correctness at the fact level, addressing these challenges. Furthermore, the Confidence-Guided Fact-level Self-Correction (ConFix) method uses high-confidence facts within a response as additional knowledge to improve low-confidence ones. This approach is evaluated across four datasets and six models, demonstrating that ConFix effectively mitigates hallucinations without requiring external knowledge sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models (LLMs) become better at judging their own answers. Right now, LLMs don’t always know how sure they are of what they’re saying. This can lead to mistakes and inaccuracies. The researchers propose a new way for LLMs to evaluate their own responses by breaking them down into smaller facts and looking at the relevance of each fact to the original question. They also develop a method that uses high-confidence answers as clues to improve low-confidence ones. In tests, this approach worked well across multiple datasets and models, reducing mistakes without needing external help. |
