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Artem Vazhentsev

9 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

A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs

EMNLP 2025

LLMs have the tendency to hallucinate, i.e., to sporadically generate false or fabricated information, and users generally lack the tools to detect when this happens. Uncertainty quantification (UQ) provides a framework for assessing the reliability of model outputs, aiding in the identification of

Cited by 0SourcePDFScholar
2025

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

NAACL 2025long

Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). To date, information-based and consistency-based UQ have been the dominant UQ methods for text generation via LLMs. Density-based methods, despite being very effective for UQ in…

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
2025

When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA

EMNLP 2025

Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grai

2023

Efficient Out-of-Domain Detection for Sequence to Sequence Models

ACL 2023findings

Sequence-to-sequence (seq2seq) models based on the Transformer architecture have become a ubiquitous tool applicable not only to classical text generation tasks such as machine translation and summarization but also to any other task where an answer can be represented in a form of a finite text frag…

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

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

NeurIPS 2022accept

This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution.…

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…