VLDB 2026 Research / reviewers in the wild / expert
Sergey Pletenev
dblp:342/5957
· DBLP profile ↗
6ranked-venue papers
2as first author
6since 2021 · last 2025
0000-0003-2325-4268ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 2 first-author · 6 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
3 papers |
Language models and text generation · 68% Question answering and dialogue systems · 27% Transfer learning and domain adaptation · 4% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › retrieval-augmented generation
adaptive retrieval |
1.7 | 2 | 2025 | LLM-Independent Adaptive RAG: Let the Question Speak for Itself · EMNLP 2025 Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home · ACL (1) 2025 |
Natural language and speech › Language models and text generation
retrieval-augmented generation |
1.7 | 2 | 2025 | LLM-Independent Adaptive RAG: Let the Question Speak for Itself · EMNLP 2025 Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home · ACL (1) 2025 |
Natural language and speech › Question answering and dialogue systems › question understanding
question classification |
0.9 | 1 | 2025 | Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA · EMNLP 2025 |
Machine learning › Transfer learning and domain adaptation › cross-lingual transfer
multilingual transfer |
0.3 | 1 | 2025 | Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA · EMNLP 2025 |
Information retrieval
query understanding |
0.3 | 1 | 2025 | LLM-Independent Adaptive RAG: Let the Question Speak for Itself · EMNLP 2025 |
Information retrieval
retrieval models |
0.3 | 1 | 2025 | LLM-Independent Adaptive RAG: Let the Question Speak for Itself · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.7uncertainty estimation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back HomeabstractRetrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. 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. We bridge this gap by conducting a comprehensive analysis of 35 adaptive retrieval methods, including 8 recent approaches and 27 uncertainty estimation techniques, across 6 datasets using 10 metrics for QA performance, self-knowledge, and efficiency. Our findings show that uncertainty estimation techniques often outperform complex pipelines in terms of efficiency and self-knowledge, while maintaining comparable QA performance. Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, Nikolay Ivanov 0001, Sergey Pletenev, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Irina Nikishina, Alexander Panchenko |
ACL (1) | 5 |
| 2025 | LLM-Independent Adaptive RAG: Let the Question Speak for ItselfabstractMaria Marina, Nikolay Ivanov, Sergey Pletenev, Mikhail Salnikov, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Maria Marina, Nikolay Ivanov 0001, Sergey Pletenev, Mikhail Salnikov, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii |
EMNLP | 3 |
| 2025 | Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QAabstractSergey Pletenev, Maria Marina, Nikolay Ivanov, Daria Galimzianova, Nikita Krayko, Mikhail Salnikov, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Sergey Pletenev, Maria Marina, Nikolay Ivanov 0001, Daria Galimzianova, Nikita Krayko, Mikhail Salnikov, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii |
EMNLP | 1 |
| 2025 | SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data AnnotatorsabstractDaniil Moskovskiy, Nikita Sushko, Sergey Pletenev, Elena Tutubalina, Alexander Panchenko. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025. Daniil Moskovskiy, Nikita Sushko, Sergey Pletenev, Elena Tutubalina, Alexander Panchenko |
NAACL (Long Papers) | 3 |
| 2025 | Memory Efficient LM Compression Using Fisher Information from Low-Rank Representations
Daniil Moskovskiy, Sergey Pletenev, Sergey Zagoruyko, Alexander Panchenko |
NLDB (1) | 2 |
| 2023 | A Computational Study of Matrix Decomposition Methods for Compression of Pre-trained Transformers
Sergey Pletenev, Viktoria Chekalina, Daniil Moskovskiy, Mikhail Seleznev, Sergey Zagoruyko, Alexander Panchenko |
PACLIC | 1 |