Summary of Multicalibration For Confidence Scoring in Llms, by Gianluca Detommaso et al.
Multicalibration for Confidence Scoring in LLMs
by Gianluca Detommaso, Martin Bertran, Riccardo Fogliato, Aaron Roth
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computation and Language (cs.CL); 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 proposed multicalibration approach yields interpretable and reliable confidence scores for outputs generated by large language models (LLMs). By simultaneously calibrating across various intersecting groupings of the data, this method improves upon traditional marginal calibration. The paper introduces two techniques for forming these groupings: clustering within an embedding space and “self-annotation” – querying the LLM via yes-or-no questions about the prompt. Novel variants of multicalibration algorithms are also developed to reduce overfitting. Through benchmarking across various question answering datasets and LLMs, this approach shows substantial improvements in fine-grained measures of calibration and accuracy compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be super helpful! But did you know they sometimes get things wrong? That’s where multicalibration comes in – it helps make sure the model is confident when it gets things right. The paper talks about a new way to do this by looking at patterns in the data and asking the model questions to understand what makes something correct or not. It also shows that this method can make the model more accurate and reliable. |
Keywords
* Artificial intelligence * Clustering * Embedding space * Overfitting * Prompt * Question answering
