Summary of Qa-calibration Of Language Model Confidence Scores, by Putra Manggala et al.
QA-Calibration of Language Model Confidence Scores
by Putra Manggala, Atalanti Mastakouri, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 In a generative question-answering system, providing accurate confidence scores is crucial for decision-making in critical applications. Existing methods aim to ensure that the average confidence score reflects the likelihood of an answer being correct. However, this approach may not be suitable for decision-making in generative QA, as it only considers the average case. To address this limitation, a new calibration method called QA-calibration is introduced, which ensures that calibration holds across different question-and-answer groups. The proposed discretized posthoc calibration schemes achieve QA-calibration and provide distribution-free guarantees on their performance. These methods are validated using confidence scores returned by elicitation prompts across multiple QA benchmarks and large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a generative question-answering system, it’s important to get the right answers most of the time. The system needs to be confident in its answers, but not too confident or too unconfident. Right now, there are ways to make sure the average answer is correct, but this doesn’t help with making decisions when it really matters. To fix this, a new way of calibrating confidence scores was developed. This new method makes sure that the confidence score reflects how likely an answer is to be correct, not just on average. The new method was tested using different question-and-answer groups and large language models. |
Keywords
» Artificial intelligence » Likelihood » Question answering
