Summary of Calibrating Expressions Of Certainty, by Peiqi Wang et al.
Calibrating Expressions of Certainty
by Peiqi Wang, Barbara D. Lam, Yingcheng Liu, Ameneh Asgari-Targhi, Rameswar Panda, William M. Wells, Tina Kapur, Polina Golland
First submitted to arxiv on: 6 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 This paper proposes a new approach to modeling linguistic expressions of certainty, such as “Maybe” and “Likely”, by representing uncertainty as distributions over the simplex. Unlike previous work, which assigns a single score to each certainty phrase, this method captures semantics more accurately. The authors generalize existing measures of miscalibration and introduce a novel post-hoc calibration method. They analyze the calibration of both humans (e.g., radiologists) and computational models (e.g., language models) and provide interpretable suggestions for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how people and machines say they’re certain about something. Right now, we assign a single score to phrases like “Maybe” or “Likely”, but this doesn’t always match what we really mean. The authors came up with a new way of showing uncertainty as a range of possibilities, which lets them measure how accurate our predictions are. They tested their method on humans and computer programs, and found ways for us all to improve our certainty scores. |
Keywords
» Artificial intelligence » Semantics
