← Search

Gleb Kuzmin

8 accepted papers

2026

Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads

ICML 2026poster

Recent progress in large language models (LLMs) has led to systems capable of producing text with remarkable fluency. However, these models are still prone to factual inaccuracies, often referred to as \``hallucinations''. One strategy to alleviate this issue is uncertainty quantification (UQ), but …

Cited by 0SourceScholar
2025

Exploring Large Language Models for Detecting Mental Disorders

EMNLP 2025

This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target

Cited by 0SourcePDFScholar
2025

Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models

NAACL 2025short

We propose selective debiasing – an inference-time safety mechanism designed to enhance the overall model quality in terms of prediction performance and fairness, especially in scenarios where retraining the model is impractical. The method draws inspiration from selective classification, where at i…

Cited by 0SourcePDFScholar
2025

Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models

EMNLP 2025

Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, be

Cited by 0SourcePDFScholar
2024

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

ACL 2024findings

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 the output being generally factually correct, making it extr…

2023

Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks

ACL 2023long

Many text classification tasks are inherently ambiguous, which results in automatic systems having a high risk of making mistakes, in spite of using advanced machine learning models. For example, toxicity detection in user-generated content is a subjective task, and notions of toxicity can be annota…

2022

Towards Computationally Feasible Deep Active Learning

NAACL 2022findings

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 others. One of such problems is the excessive computational…

2022

Uncertainty Estimation of Transformer Predictions for Misclassification Detection

ACL 2022long

Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these metho…