Papers by Mikhail Burtsev
Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity (2025.acl-long)
Copied to clipboard
| Challenge: | Recent work addresses problem of compression of tokens into shorter sequence of real-valued vectors . attainable lossless compression ratio is typically not higher than x10 . |
| Approach: | They propose to compress a sequence of tokens into a shorter sequence of real-valued vectors to be used as inputs instead of token embeddings or key-value cache. |
| Outcome: | The proposed algorithms reduce the amount of compute in existing language models rather than minimizing number of bits needed to store text. |
Attention Understands Semantic Relations (2022.lrec-1)
Copied to clipboard
| Challenge: | Present-day monopoly of foundation language models in most tasks forces researchers and practitioners to rely on popular large models without genuinely understanding the models' behaviour. |
| Approach: | They propose a probing pipeline to study the representedness of semantic relations in transformer language models and propose 'attention mechanisms' that focus on syntactic relational information and semantic one. |
| Outcome: | The proposed pipeline shows that attention scores are expressive as output activations on this task, despite their lesser ability to represent surface cues. |
DeepPavlov: Open-Source Library for Dialogue Systems (P18-4)
Copied to clipboard
Mikhail Burtsev, Alexander Seliverstov, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurzina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, Alexey Litinsky, Varvara Logacheva, Alexey Lymar, Valentin Malykh, Maxim Petrov, Vadim Polulyakh, Leonid Pugachev, Alexey Sorokin, Maria Vikhreva, Marat Zaynutdinov
| Challenge: | open-source library DeepPavlov is designed for rapid development of dialogue systems. |
| Approach: | open-source library DeepPavlov is tailored for development of conversational agents . the library prioritizes efficiency, modularity and extensibility with the goal to make it easier to develop dialogue systems from scratch . |
| Outcome: | the open-source library DeepPavlov is designed for rapid development of dialogue systems . it supports modular as well as end-to-end approaches to implementation of conversational agents . |
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks (2023.acl-long)
Copied to clipboard
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. |
Uncertainty Guided Global Memory Improves Multi-Hop Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Transformers are used to solve multi-hop question answering tasks that require reasoning over multiple parts of a long document. |
| Approach: | They propose a method that collects relevant information over the entire document and then combines it with local context to solve a multi-hop question answering task. |
| Outcome: | The proposed method improves on three MHQA datasets compared to the baseline model. |
Uncertainty Estimation of Transformer Predictions for Misclassification Detection (2022.acl-long)
Copied to clipboard
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. |
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances on neural approaches to natural language processing have triggered a renaissance in end-to-end neural open-domain chatbots. |
| Approach: | They propose to use offline and online steps to evaluate the quality of clarifying questions in various open-domain dialogues to improve the quality and accuracy of the system response. |
| Outcome: | The proposed pipeline is suitable as a foundation for further research. |
Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph completion (KGC) methods are computationally intensive and impractical for large-scale KGs. |
| Approach: | They propose to include node neighborhoods as additional information to improve KGC methods based on language models. |
| Outcome: | The proposed method outperforms KGT5 and conventional methods on inductive and transductive Wikidata subsets and shows its importance. |
DeepPavlov Dream: Platform for Building Generative AI Assistants (2023.acl-demo)
Copied to clipboard
Diliara Zharikova, Daniel Kornev, Fedor Ignatov, Maxim Talimanchuk, Dmitry Evseev, Ksenya Petukhova, Veronika Smilga, Dmitry Karpov, Yana Shishkina, Dmitry Kosenko, Mikhail Burtsev
| Challenge: | open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform supports modular approach to implementation of conversational agents . |
| Approach: | open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform includes a conversational orchestrator called DeepPvlov Agent to coordinate asynchronous dialog pipeline . |
| Outcome: | The open-source DeepPavlov Dream Platform is designed for development of complex dialog systems like Generative AI Assistants. |
Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Knowledge graphs provide structured, verifiable grounding for large language models . current LLMs use KGs as auxiliary structures for text retrieval . |
| Approach: | They propose a pipeline that constructs KGs from open-domain texts using triplets and qualifiers. |
| Outcome: | The proposed pipeline outperforms existing methods in retrieval-augmented generation. |
Active Learning for Abstractive Text Summarization (2022.findings-emnlp)
Copied to clipboard
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. |
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling (2026.findings-acl)
Copied to clipboard
Ivan Rodkin, Daniil Orel, Konstantin Smirnov, Arman Bolatov, Bilal Elbouardi, Besher Hassan, Yuri Kuratov, Aydar Bulatov, Preslav Nakov, Timothy Baldwin, Artem Shelmanov, Mikhail Burtsev
| Challenge: | Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. |
| Approach: | They evaluate how large language models learn multi-step reasoning without memorization . they find that most neural architectures trained from scratch can learn rule inference . |
| Outcome: | The proposed framework fails to solve a natural-language proxy task with high accuracy. |