Summary of Does Confidence Calibration Improve Conformal Prediction?, by Huajun Xi et al.
Does confidence calibration improve conformal prediction?
by Huajun Xi, Jianguo Huang, Kangdao Liu, Lei Feng, Hongxin Wei
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates the impact of confidence calibration on adaptive conformal prediction, a technique for uncertainty quantification. The authors discover that current confidence calibration methods typically lead to larger prediction sets and show that high-confidence predictions can enhance efficiency. They propose Conformal Temperature Scaling (ConfTS) as a solution, which optimizes parameters to generate efficient prediction sets. This method improves existing adaptive conformal prediction techniques in classification tasks, especially with large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is a way to be sure about how good our predictions are. Some people use temperature scaling to make these predictions better. But what does this do for the uncertainty? The researchers found out that using temperature scaling makes the predictions bigger than they should be. They also discovered that when we have high-confidence predictions, it helps us get more accurate results. This is important because we want our predictions to be as good as possible. To fix this problem, they created a new way of doing temperature scaling called Conformal Temperature Scaling (ConfTS). This makes the predictions better and more efficient. |
Keywords
* Artificial intelligence * Classification * Temperature
