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stat.ML2025★ 1 cited
Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
Ekaterina Fadeeva, Maiya Goloburda, Aleksandr Rubashevskii +5
Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obt…
stat.ML2023
Efficient Conformal Prediction under Data Heterogeneity
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii +7
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods…