Papers by Nikita Krayko
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. |
Efficient Answer Retrieval System (EARS): Combining Local DB Search and Web Search for Generative QA (2024.emnlp-industry)
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| Challenge: | Developing a virtual assistant is crucial for supporting clients as it provides 24/7 assistance . factual questionanswering system is capable of handling all user queries . |
| Approach: | They propose a production-ready factual question answering system that combines local knowledge base search with generative, context-based QA. |
| Outcome: | The proposed system boosts local knowledge base retrieval by 23% . the system is language-agnostic and can be applied to any data domain . |