Uncertainty quantification (UQ) and calibration for large language models — a simple, information-theoretic method that aggregates multiple LLMs for reliable confidence estimates in high-stakes decision-making.
Lead: This project was led by Maya Kruse, a former NLP Data Scientist at the LARK Lab.