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Alexander Panchenko

31 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
2026

Harnessing Non-Adversarial Robustness in Large Language Models

ICML 2026spotlight

The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by semantically similar but textually different prompts. Recent works have shown that these kinds of prompt variations can significantly impact the perf…

Cited by 0SourceScholar
2025

Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home

ACL 2025long

Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. Recent adaptive retrieval methods in…

2025

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

ACL 2025long

People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition–an umbrella term for several NLP tasks–impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities…

2025

HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

ACL 2025finding

Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking…

2025

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

NAACL 2025findings

The performance of Large Language Models (LLMs) on many tasks is greatly limited by the knowledge learned during pre-training and stored in the model’s parameters. Low-rank adaptation (LoRA) is a popular and efficient training technique for updating or domain-specific adaptation of LLMs. In this stu…

2025

LLM-Independent Adaptive RAG: Let the Question Speak for Itself

EMNLP 2025

Large Language Models (LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformation. Adaptive retrieval aims to retrieve only when necessary, but existing approaches rely on LLM-based uncertainty estima

2025

Multilingual and Explainable Text Detoxification with Parallel Corpora

COLING 2025main

Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022), digital abusive speech remains a significant issue. One potential approach to address this challenge is automatic text detoxifi…

2025

SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators

NAACL 2025long

Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. In this work, we introduce a pipeline for the generation of multilingual parallel detoxification data. We also introduce SynthDetoxM, a manually collected and synthetically generat…

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

2025

When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs

EMNLP 2025

Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 4 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 mo

2025

Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA

EMNLP 2025

Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions – whether they are evergreen (answers remain stable over time) or mutable (answers change). In this work, we introduce EverGreenQA, the

2024

Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning

COLING 2024main

Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for…

2024

CAM 2.0: End-to-End Open Domain Comparative Question Answering System

COLING 2024main

Comparative Question Answering (CompQA) is a Natural Language Processing task that combines Question Answering and Argument Mining approaches to answer subjective comparative questions in an efficient argumentative manner. In this paper, we present an end-to-end (full pipeline) system for answering…

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…

2024

LLMs to Replace Crowdsourcing For Parallel Data Creation? The Case of Text Detoxification

EMNLP 2024finding

The lack of high-quality training data remains a significant challenge in NLP. Manual annotation methods, such as crowdsourcing, are costly, require intricate task design skills, and, if used incorrectly, may result in poor data quality. From the other hand, LLMs have demonstrated proficiency in man…

2024

MERA: A Comprehensive LLM Evaluation in Russian

ACL 2024long

Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers’ attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still…

2024

MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages

NAACL 2024short

Text detoxification is a textual style transfer (TST) task where a text is paraphrased from a toxic surface form, e.g. featuring rude words, to the neutral register. Recently, text detoxification methods found their applications in various task such as detoxification of Large Language Models (LLMs)…

2024

SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers

EMNLP 2024main

The performance of Transformer models has been enhanced by increasing the number of parameters and the length of the processed text. Consequently, fine-tuning the entire model becomes a memory-intensive process. High-performance methods for parameter-efficient fine-tuning (PEFT) typically work with…

2024

TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

ACL 2024long

In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experiments, we present TaxoLLaMA, the “all-in-one” model for taxonomy-related tasks, l…

2024

xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics

EMNLP 2024main

State-of-the-art trainable machine translation evaluation metrics like xCOMET achieve high correlation with human judgment but rely on large encoders (up to 10.7B parameters), making them computationally expensive and inaccessible to researchers with limited resources. To address this issue, we inve…

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

Active Learning for Abstractive Text Summarization

EMNLP 2022finding

Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annotator to read a long document and compose a shorter summary that would preserve the key information relayed by the origin…

2022

ParaDetox: Detoxification with Parallel Data

ACL 2022long

We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We…

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…

2021

Text Detoxification using Large Pre-trained Neural Models

EMNLP 2021main

We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphras…

2020

Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution

COLING 2020main

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data…

2020

Studying Taxonomy Enrichment on Diachronic WordNet Versions

COLING 2020main

Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy…