Fact-Level Confidence Calibration and Self-Correction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929599169953792 |
|---|---|
| author | Yuan, Yige Xu, Bingbing Tan, Hexiang Sun, Fei Xiao, Teng Li, Wei Shen, Huawei Cheng, Xueqi |
| author_facet | Yuan, Yige Xu, Bingbing Tan, Hexiang Sun, Fei Xiao, Teng Li, Wei Shen, Huawei Cheng, Xueqi |
| contents | Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their outputs. However, current calibration methods for LLMs typically estimate two scalars to represent overall response confidence and correctness, which is inadequate for long-form generation where the response includes multiple atomic facts and may be partially confident and correct. These methods also overlook the relevance of each fact to the query. To address these challenges, we propose a Fact-Level Calibration framework that operates at a finer granularity, calibrating confidence to relevance-weighted correctness at the fact level. Furthermore, comprehensive analysis under the framework inspired the development of Confidence-Guided Fact-level Self-Correction ($\textbf{ConFix}$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones. Extensive experiments across four datasets and six models demonstrate that ConFix effectively mitigates hallucinations without requiring external knowledge sources such as retrieval systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13343 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fact-Level Confidence Calibration and Self-Correction Yuan, Yige Xu, Bingbing Tan, Hexiang Sun, Fei Xiao, Teng Li, Wei Shen, Huawei Cheng, Xueqi Computation and Language Artificial Intelligence Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their outputs. However, current calibration methods for LLMs typically estimate two scalars to represent overall response confidence and correctness, which is inadequate for long-form generation where the response includes multiple atomic facts and may be partially confident and correct. These methods also overlook the relevance of each fact to the query. To address these challenges, we propose a Fact-Level Calibration framework that operates at a finer granularity, calibrating confidence to relevance-weighted correctness at the fact level. Furthermore, comprehensive analysis under the framework inspired the development of Confidence-Guided Fact-level Self-Correction ($\textbf{ConFix}$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones. Extensive experiments across four datasets and six models demonstrate that ConFix effectively mitigates hallucinations without requiring external knowledge sources such as retrieval systems. |
| title | Fact-Level Confidence Calibration and Self-Correction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.13343 |