Roman Vashurin

Embedded Ensembles: Infinite Width Limit and Operating Regimes

2022 · arXiv (Cornell University)

A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as Embedded Ensembling (EE); its particular …

LM-Polygraph: Uncertainty Estimation for Language Models

2023 · arXiv (Cornell University)

Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often "hallucinate", i.e., fabricate …

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

2024 · arXiv (Cornell University)

The rapid proliferation of large language models (LLMs) has stimulated researchers to seek effective and efficient approaches to deal with LLM hallucinations and low-quality outputs. Uncertainty quantification (UQ) is a key element of machine learning …