Summary of Calibrated Large Language Models For Binary Question Answering, by Patrizio Giovannotti and Alexander Gammerman
Calibrated Large Language Models for Binary Question Answering
by Patrizio Giovannotti, Alexander Gammerman
First submitted to arxiv on: 1 Jul 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 proposed approach utilizes the inductive Venn–Abers predictor (IVAP) to calibrate the probabilities associated with output tokens corresponding to binary labels in large language models (LLMs). The goal is to align predicted probabilities with actual correctness, ensuring a well-calibrated model that accurately reflects prediction likelihood. Experimental results on the BoolQ dataset using the Llama 2 model show IVAP outperforming temperature scaling for various label token choices, achieving reliable uncertainty estimates while maintaining high predictive quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting really good at predicting what we mean when we write or talk. But sometimes they’re not sure about their answers and that’s okay! They need help figuring out how likely they are to be right. This paper shows a new way to do this called IVAP, which helps LLMs become more accurate in their predictions. |
Keywords
» Artificial intelligence » Likelihood » Llama » Temperature » Token
