Papers by Ivanov Mikhail
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home (2025.acl-long)
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Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, Nikolay Ivanov, Sergey Pletenev, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Irina Nikishina, Alexander Panchenko
| Challenge: | Recent adaptive retrieval methods integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge, but they often neglect efficiency evaluations and comparisons with uncertainty estimation techniques. |
| Approach: | They propose to integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge but neglect efficiency evaluations and comparisons with uncertainty estimation techniques. |
| Outcome: | The proposed methods outperform complex pipelines in terms of efficiency and self-knowledge while maintaining comparable QA performance. |
Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA (2025.emnlp-main)
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Sergey Pletenev, Maria Marina, Nikolay Ivanov, Daria Galimzianova, Nikita Krayko, Mikhail Salnikov, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii
| Challenge: | Large Language Models (LLMs) often struggle with question answering due to hallucinated answers. |
| Approach: | They propose a multilingual QA dataset with evergreen labels that can be used to evaluate and train large language models. |
| Outcome: | The proposed model performs well on 12 modern LLMs and EG-E5 classifiers. |
LLM-Independent Adaptive RAG: Let the Question Speak for Itself (2025.emnlp-main)
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Maria Marina, Nikolay Ivanov, Sergey Pletenev, Mikhail Salnikov, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii
| Challenge: | Existing methods to retrieve Large Language Models (LLMs) are inefficient and impractical. |
| Approach: | They propose a lightweight adaptive retrieval method that leverages external information to achieve comparable quality while achieving significant efficiency gains. |
| Outcome: | The proposed methods achieve comparable quality while achieving significant efficiency gains on 6 QA datasets. |
SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks (2025.emnlp-demos)
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Adamenko Pavel, Ivanov Mikhail, Aidar Valeev, Rodion Levichev, Pavel Zadorozhny, Ivan Lopatin, Dmitrii Babaev, Alena Fenogenova, Valentin Malykh
| Challenge: | SWE-bench is a static benchmark that collects only once and never updates. |
| Approach: | They propose a dynamic, continuously updated benchmark to address data contamination issues by collecting real-world GitHub issues and rigorous quality validation. |
| Outcome: | The proposed benchmarks are based on a dataset of 2,294 GitHub issues and their corresponding pull requests (PRs) the static nature of the benchmarks makes it hard to distinguish meaningful progress. |