Papers by Ivan Tsvigun
A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs (2025.emnlp-main)
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Artem Shelmanov, Ekaterina Fadeeva, Akim Tsvigun, Ivan Tsvigun, Zhuohan Xie, Igor Kiselev, Nico Daheim, Caiqi Zhang, Artem Vazhentsev, Mrinmaya Sachan, Preslav Nakov, Timothy Baldwin
| Challenge: | Uncertainty quantification (UQ) is a framework for assessing the reliability of model outputs. |
| Approach: | They introduce pre-trained UQ heads for LLMs that are highly robust and generalized to languages they were not explicitly trained on. |
| Outcome: | The pre-trained heads significantly improve their ability to capture uncertainty compared to unsupervised methods. |
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. |
ATGen: A Framework for Active Text Generation (2025.acl-demo)
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Akim Tsvigun, Daniil Vasilev, Ivan Tsvigun, Ivan Lysenko, Talgat Bektleuov, Aleksandr Medvedev, Uliana Vinogradova, Nikita Severin, Mikhail Mozikov, Andrey Savchenko, Ilya Makarov, Grigorev Rostislav, Ramil Kuleev, Fedor Zhdanov, Artem Shelmanov
| Challenge: | Despite the surging popularity of natural language generation tasks, the application of active learning (AL) to NLG has been limited. |
| Approach: | They propose a framework that bridges AL with text generation tasks and provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. |
| Outcome: | The proposed framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). |
ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts (2022.emnlp-demos)
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Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova, Dmitry V. Dylov, Igor Galitskiy, Artem Shelmanov
| Challenge: | Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. |
| Approach: | They propose an open-source framework for active learning annotation in natural language processing that provides an easy-to-deploy GUI annotation tool directly in the Jupyter IDE. |
| Outcome: | The proposed framework reduces computational overhead and duration of AL iterations and increases annotated data reusability. |