Papers by Maxim Panov
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models (2025.naacl-long)
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Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). |
| Approach: | They propose to use a well-established method for text generation to extract token embeddings from multiple layers of LLMs and compute MD scores for each token. |
| Outcome: | The proposed method improves on existing methods and provides accurate and computationally efficient uncertainty scores for sequence-level selective generation and claim-level fact-checking tasks. |
How Certain is Your Transformer? (2021.eacl-main)
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Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov
| Challenge: | Obtaining reliable uncertainty estimations for such neural networks (NNs) is challenging due to the huge number of parameters in these deep learning models. |
| Approach: | They propose to use Monte Carlo dropout to estimate uncertainty for Transformer-based models and construct inexpensive estimates using Determinantal Point Processes. |
| Outcome: | The proposed estimates improve the quality of detection of error-prone instances. |
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) generate misleading or outright incorrect information. |
| Approach: | They propose a method that debiases uncertainty scores on output length and uses residuals as corrected, length-invariant estimates. |
| Outcome: | The proposed method improves over nominally length-normalized methods on machine translation, summarization, and question-answering tasks. |
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks (2023.acl-long)
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Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Existing methods for text classification tasks are inherently ambiguous and can cause errors. |
| Approach: | They propose a method that combines epistemic and aleatoric uncertainty to estimate toxicity detection errors. |
| Outcome: | The proposed method outperforms existing methods for toxicity detection and other ambiguous text classification tasks. |
LM-Polygraph: Uncertainty Estimation for Language Models (2023.emnlp-demo)
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Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Large language models often "hallucinate" i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. |
| Approach: | They propose a framework with implementations of state-of-the-art UE methods for LLMs with unified program interfaces in Python. |
| Outcome: | The proposed framework implements state-of-the-art UE methods for LLMs with unified program interfaces in Python and an extendable benchmark for consistent evaluation by researchers. |
Uncertainty Estimation of Transformer Predictions for Misclassification Detection (2022.acl-long)
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Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Uncertainty estimation (UE) of model predictions is crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, etc. |
| Approach: | They propose to modify UE methods for Transformer models for misclassification detection in named entity recognition and text classification tasks to improve model expressiveness and computational performance. |
| Outcome: | The proposed methods outperform computationally intensive methods on misclassification detection tasks and are based on a large dataset of simulated datasets. |
Uncertainty Quantification for Large Language Models (2025.acl-tutorials)
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| Challenge: | Large language models (LLMs) produce hallucinations, which undermine user trust and reliability. |
| Approach: | This tutorial offers the first systematic introduction to uncertainty quantification (UQ) for LLMs in text generation tasks. |
| Outcome: | The proposed framework provides tools for communicating the reliability of a model answer. |
Efficient Out-of-Domain Detection for Sequence to Sequence Models (2023.findings-acl)
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Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, Artem Shelmanov
| Challenge: | Sequence-to-sequence (seq2sequ) models are a ubiquitous tool for text generation but they are not suitable for many other tasks. |
| Approach: | They propose to use UE techniques to identify out-of-domain (OOD) inputs where the model is susceptible to errors. |
| Outcome: | The proposed methods outperform heavyweight ensembles on the task of OOD detection. |
Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models (2025.emnlp-main)
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Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, Gleb Kuzmin, Ivan Lazichny, Alexander Panchenko, Preslav Nakov, Timothy Baldwin, Maxim Panov, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a promising approach for detecting hallucinations and low-quality outputs of Large Language Models (LLMs). |
| Approach: | They propose to learn conditional dependency between autoregressive LLM generation steps from attention-based features and a two-staged training procedure to incorporate recurrent features. |
| Outcome: | The proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches. |
Active Learning for Abstractive Text Summarization (2022.findings-emnlp)
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Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Abstractive text summarization (ATS) requires a long document and short summaries. |
| Approach: | They propose a query strategy for AL in abstractive text summarization that uses uncertainty estimation to reduce model performance. |
| Outcome: | The proposed query strategy improves ROUGE and consistency scores for annotated datasets . it also increases the performance of the model, compared to passive annotation. |
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification (2024.findings-acl)
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Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov
| Challenge: | Large language models are notorious for producing erroneous claims in their output. |
| Approach: | They propose a fact-checking and hallucination detection pipeline based on token-level uncertainty quantification that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. |
| Outcome: | The proposed method can fact-check the atomic claims in the output of large language models. |
Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval-Augmented Generation (2026.findings-acl)
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Ekaterina Fadeeva, Aleksandr Rubashevskii, Dzianis Piatrashyn, Roman Vashurin, Shehzaad Dhuliawala, Artem Shelmanov, Timothy Baldwin, Preslav Nakov, Mrinmaya Sachan, Maxim Panov
| Challenge: | Existing approaches to mitigating hallucinations conflate factuality with faithfulness to the retrieved evidence, incorrectly labeling factually correct statements as hallucinos . Existing methods to mitigate hallucinics rely on a lack of training data coverage, input ambiguity, and architectural constraints. |
| Approach: | They propose a method for hallucination detection in Large Language Models enhanced with knowledge retrieval based on faithfulness to the retrieved context. |
| Outcome: | The proposed method outperforms unsupervised UQ baselines, RAG-specific methods, and supervised classifiers across multiple tasks and LLMs. |
Reference-free Hallucination Detection for Large Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Large vision-language models exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, but they are prone to producing hallucinations. |
| Approach: | They propose to use supervised uncertainty quantification methods to detect hallucinations in large vision-language models. |
| Outcome: | The proposed methods outperform the others in detecting hallucinations on four representative LVLMs across two different tasks. |