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Katya Mirylenka

2 accepted papers

2025

SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models

EMNLP 2025

When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output, and is therefore increasingly recognized as a crucial component of trusted AI systems. Black-box UQ

Cited by 0SourcePDFScholar
2025

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models

UAI 2025

Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) methods, relying solely on model API access, have gained popularity due to their practical benefits. In this paper, we examine

Cited by 0SourcePDFScholar