Artem Shelmanov

Towards Computationally Feasible Deep Active Learning

2022 · Findings of the Association for Computational Linguistics: NAACL 2022

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many …

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 …

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

2024 · arXiv (Cornell University)

Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of …

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 …

How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering

2025

Irina Nikishina, Saba Anwar, Nikolay Dolgov, Maria Manina, Daria Ignatenko, Artem Shelmanov, Chris Biemann. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.