Study explores confidence estimation for LLMs in math question answering

A new study published on arXiv investigates methods for improving confidence estimation in large language models (LLMs) when answering mathematical questions. Researchers found that while individual token probabilities are often overconfident, aggregating these probabilities across an entire sequence can provide informative confidence estimates. The study also explored multi-pass methods like self-verification and Monte Carlo Dropout, as well as post-hoc calibration techniques such as Platt scaling and isotonic regression, which significantly reduced calibration errors. AI

IMPACT Provides insights into improving the reliability and trustworthiness of LLM outputs for critical tasks like mathematical problem-solving.

RANK_REASON Academic paper detailing empirical study of LLM confidence estimation methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Hugging Face
• Isotonic regression
• large-language models
• Monte Carlo Dropout
• Platt scaling

• paper
• model release

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