Anoop Mayampurath

Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification

Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on calibration and uncertainty quantification often focuses on individual models, overlooking the potential of model diver