Papers by Katya Mirylenka
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models (2025.findings-emnlp)
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Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass, Xiao Shou
| Challenge: | Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output. |
| Approach: | They propose a black-box approach where consistency is used as a proxy for confidence in a model's output. |
| Outcome: | The proposed methods are primarily but not necessarily entirely black- box, with consistency between output and other sampled generations used as a proxy for confidence in its correctness. |