Summary of Uncertainty in Language Models: Assessment Through Rank-calibration, by Xinmeng Huang et al.
Uncertainty in Language Models: Assessment through Rank-Calibration
by Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 proposed Rank-Calibration framework assesses uncertainty and confidence measures for Language Models (LMs) in natural language generation tasks. This is crucial as LMs often generate incorrect or hallucinated responses, making it essential to quantify their uncertainty accurately. The paper highlights the need to compare various uncertainty measures, such as semantic entropy and affinity-graph-based measures, which take values over different ranges. To address this issue, the authors develop a novel framework that quantifies deviations from an ideal relationship between uncertainty and generation quality. This framework is demonstrated empirically for its broad applicability and granular interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language Models (LMs) are super smart computers that can generate human-like language. But sometimes they make mistakes or create fake responses. To fix this, scientists need to figure out how confident these models are in their answers. There are many ways to measure confidence, but they all work differently. This paper introduces a new way to compare these methods and shows that it works well for different tasks. |
