Search Results for author:
Found 4 papers, 2 papers with code
CoCoA: A Generalized Approach to Uncertainty Quantification by Integrating Confidence and Consistency of LLM Outputs
Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompasses a variety of approaches, with two major types being particularly prominent: information-based, which focus on model confidence expressed as token probabilities, and consistency-based, which assess the semantic relationship between multiple outputs generated using repeated sampling.
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Using our benchmark, we conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
LM-Polygraph: Uncertainty Estimation for Language Models
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields.
Embedded Ensembles: Infinite Width Limit and Operating Regimes
In this limit, we identify two ensemble regimes - independent and collective - depending on the architecture and initialization strategy of ensemble models.
