57 citations · 90 across the 14 of their papers we have counts for
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cs.CL2025★ 1 cited
Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods
Nicola Cecere, Andrea Bacciu, Ignacio Fernández Tobías +1
Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs c…
stat.ML2025
The Majority Vote Paradigm Shift: When Popular Meets Optimal
Antonio Purificato, Maria Sofia Bucarelli, Anil Kumar Nelakanti +3
Reliably labelling data typically requires annotations from multiple human workers. However, humans are far from being perfect. Hence, it is a common practice to aggregate labels g…