Summary of On Subjective Uncertainty Quantification and Calibration in Natural Language Generation, by Ziyu Wang et al.
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
by Ziyu Wang, Chris Holmes
First submitted to arxiv on: 7 Jun 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 research addresses challenges in quantifying uncertainties when large language models generate free-form responses. By assuming a similarity measure between generated and true responses, the study enables principled uncertainty quantification and calibration using Bayesian decision theory. The methods can be applied to black-box language models and are illustrated on question answering and machine translation tasks. The research provides a principled evaluation of task-specific calibration and demonstrates that epistemic uncertainty offers a promising deferral strategy for efficient data acquisition in in-context learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with large language models that generate answers to questions or translate text from one language to another. These models often don’t know how sure they are about their answers, which makes it hard to trust them. The researchers found a way to fix this by using a special type of math called Bayesian decision theory. They showed how this method can be used with black-box language models and tested it on two types of tasks: answering questions and translating text. This research helps us understand when we should trust the answers from these models and makes it easier to learn new things. |
Keywords
» Artificial intelligence » Question answering » Translation
