Summary of Relying on the Unreliable: the Impact Of Language Models’ Reluctance to Express Uncertainty, by Kaitlyn Zhou et al.
Relying on the Unreliable: The Impact of Language Models’ Reluctance to Express Uncertainty
by Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Maarten Sap
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 research investigates how large language models (LMs) communicate uncertainties in their responses, particularly in downstream applications. The study finds that LMs are reluctant to express uncertainties when answering questions, even when producing incorrect responses. When prompted to express confidences, LMs tend to be overconfident, leading to high error rates among confident responses. Human experiments reveal users rely heavily on LM generations, regardless of certainty markers. Additionally, the study identifies a bias against texts with uncertainty in preference-annotated datasets used for post-training alignment. The work highlights safety concerns and proposes design recommendations to mitigate these risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are important tools that help us communicate with artificial intelligence. But how do they tell us when they’re not sure about something? Researchers looked at how LMs answer questions and found that they often don’t say “I’m not sure” even when they get the answer wrong. When asked to show their confidence, LMs tend to be too confident and make mistakes. People trust what LMs say, so it’s important to design them in a way that makes sense. |
Keywords
* Artificial intelligence * Alignment
