Summary of Confidence in the Reasoning Of Large Language Models, by Yudi Pawitan and Chris Holmes
Confidence in the Reasoning of Large Language Models
by Yudi Pawitan, Chris Holmes
First submitted to arxiv on: 19 Dec 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 A novel study examines the extent to which large language models (LLMs) demonstrate confidence in their responses, exploring both quantitative and qualitative measures. The researchers investigate three LLMs’ performance on benchmark questions in causal judgement, formal fallacies, probability, statistical puzzles, and paradoxes. While LLMs outperform random guessing, they exhibit significant variability in changing their initial answers. A positive correlation is found between confidence and accuracy, but the overall accuracy for the second answer is often worse than the first. The study also reveals a strong tendency to overstate self-reported confidence scores. These findings indicate that current LLMs lack an internally coherent sense of confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at answering questions, but how confident are they in their answers? Researchers studied three big models and found they’re not always sure about what they know. They asked the models to explain why they thought something was true or false, and some models changed their minds when given more information. This study shows that while the models are good at answering questions, they don’t really understand how confident they should be in their answers. |
Keywords
» Artificial intelligence » Probability
