Summary of Cycles Of Thought: Measuring Llm Confidence Through Stable Explanations, by Evan Becker et al.
Cycles of Thought: Measuring LLM Confidence through Stable Explanations
by Evan Becker, Stefano Soatto
First submitted to arxiv on: 5 Jun 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 The paper proposes a novel framework for measuring the uncertainty of large language models (LLMs) in high-risk applications where overconfidence can lead to incorrect predictions. While traditional methods are not directly applicable due to computational costs and closed-source nature, the authors develop a black-box approach using explanation entailment as a test-time classifier. This allows for calculating a posterior answer distribution over the most likely model+explanation pairs, improving confidence score metrics (AURC and AUROC) across five datasets compared to baselines. The framework is principled, effective, and has implications for quantifying uncertainty in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a problem with big language models that makes them overconfident when they’re wrong. This can be bad because it means the model thinks it’s right even when it’s not. The researchers propose a new way to measure how sure or unsure these models are, using something called explanations. They test their method on five different datasets and find that it works better than other methods. This is important because it could help us create more reliable language models in the future. |
