Papers by Elisei Rykov
Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images (2025.naacl-srw)
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Elisei Rykov, Kseniia Petrushina, Kseniia Titova, Anton Razzhigaev, Alexander Panchenko, Vasily Konovalov
| Challenge: | Existing methods to measure image common sense inconsistentness are difficult to implement because of their complexity. |
| Approach: | They propose a visual commonsense model that leverages large vision-language models to extract atomic facts from images and a compact attention-pooling classifier to fine-tune it over encoded atomic fact. |
| Outcome: | The proposed method outperforms existing methods on the WHOOPS! and WEIRD datasets while maintaining a compact attention-pooling classifier over encoded atomic facts. |
Fine-Grained Semantic Comparison of Legal Documents using LLMs (2026.acl-srw)
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Elisei Rykov, Nikolay Ivanov, Maria Bandulevich, Kseniia Petrushina, Valentin Malykh, Vasily Konovalov, Alexander Panchenko, Ilseyar Alimova
| Challenge: | Existing tools for detecting inconsistencies and contradictions in complex regulatory documents rely on character-level diffs. |
| Approach: | They propose a benchmark to evaluate span-aware semantic comparison of legal documents . legDiff is an annotated pair of legal paragraphs that is automatically generated . |
| Outcome: | The proposed benchmark evaluates span-aware semantic comparisons of legal documents . it generates synthetic training data that aligns with the manual annotations and mirrors the structure and label distribution of the benchmark . |
Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows (2026.acl-industry)
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Nikita Borovkov, Elisei Rykov, Olga Tsymboi, Sergei Filimonov, Nikita Surnachev, Dmitry Bitman, Anatolii Potapov
| Challenge: | 45% of sessions automated in production without degrading support quality level . traditional automated processes are costly at scale and require manual rule authoring . |
| Approach: | They propose a system that automates end-to-end customer support workflows inside an enterprise BPM platform. |
| Outcome: | The proposed system automates 45% of sessions and reduces average handling time by 39% without degrading support quality level. |
When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA (2025.findings-emnlp)
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Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov, Artem Vazhentsev, Kseniia Titova, Alexander Panchenko, Vasily Konovalov, Julia Belikova
| Challenge: | Existing hallucination detection benchmarks operate at the sequence level and are limited to English . Existing methods lacking fine-grained, multilingual supervision are limited in English based on the sequence . |
| Approach: | They propose a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. |
| Outcome: | The proposed dataset annotated with span-level hallucinations across 14 languages is scalable and cost-efficient. |
Multimodal Evaluation of Russian-language Architectures (2026.eacl-long)
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Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin, Maria Tikhonova, Alexander Kharitonov, Yulia Lyakh, Petr Surovtsev, Denis Shevelev, Vildan Saburov, Vasily Konovalov, Elisei Rykov, Ivan Sviridov, Amina Miftakhova, Ilseyar Alimova, Alexander Panchenko, Alexander Kapitanov, Alena Fenogenova
| Challenge: | Multimodal large language models (MLLMs) are at the center of research attention, yet intelligence, limitations, and risks remain insufficiently understood. |
| Approach: | They propose an open multimodal evaluation framework for Russian-spoken architectures . the framework is instruction-based and includes 18 newly constructed evaluation tasks . |
| Outcome: | The proposed framework provides a replicable methodology for constructing multimodal benchmarks in Russian-spoken architectures. |