Summary of Confidence Under the Hood: An Investigation Into the Confidence-probability Alignment in Large Language Models, by Abhishek Kumar et al.
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
by Abhishek Kumar, Robert Morabito, Sanzhar Umbet, Jad Kabbara, Ali Emami
First submitted to arxiv on: 25 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 research explores how Large Language Models (LLMs) assess their confidence in generated responses. The study introduces Confidence-Probability Alignment, a concept that links an LLM’s internal confidence quantified by token probabilities to the confidence expressed in its response when asked about certainty. Various datasets and prompting techniques are used to probe the alignment between models’ internal and expressed confidence. The results show that OpenAI’s GPT-4 has the strongest confidence-probability alignment, with an average Spearman’s ρ of 0.42 across different tasks. This work contributes to understanding model trustworthiness and facilitating risk assessment in LLM applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models think about their own answers. The researchers found a way to measure how sure the model is about its response, both from inside the model’s thinking and by asking it directly. They tested this idea using different datasets and ways of asking the questions. One important finding was that OpenAI’s GPT-4 did a good job at matching its internal thoughts with what it said about being confident. This research helps us understand how to use these models safely and make better decisions. |
Keywords
» Artificial intelligence » Alignment » Gpt » Probability » Prompting » Token
