Papers by Daniil Vasilev
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. |
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. |
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024.findings-naacl)
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Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko
| Challenge: | Existing methods to segment textual data are difficult to handle for noisy spoken dialogues. |
| Approach: | They propose to leverage dialogue summaries for unsupervised topic segmentation . they show that the new approach outperforms state-of-the-art methods in unsupervised segmentation and requires less setup . |
| Outcome: | The proposed approach outperforms state-of-the-art methods in unsupervised topic segmentation and requires less setup. |
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). |