4 citations · 7 across the 15 of their papers we have counts for
16 papers
Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
Ivan Sedykh, Nikita Sorokin, Valentin Malykh
Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive because generation requires many full…
Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation
Nikita Sorokin, Ivan Sedykh, Valentin Malykh
Retrieval-augmented code generation often conditions the decoder on large retrieved code snippets. This ties online inference cost to repository size and introduces noise from long…
AINL-Eval 2025 Shared Task: Detection of AI-Generated Scientific Abstracts in Russian
Tatiana Batura, Elena Bruches, Milana Shvenk +1
The rapid advancement of large language models (LLMs) has revolutionized text generation, making it increasingly difficult to distinguish between human- and AI-generated content. T…
MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Artem Chervyakov, Alexander Kharitonov, Pavel Zadorozhny +20
Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most b…
SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
Pavel Adamenko, Mikhail Ivanov, Aidar Valeev +6
The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench datas…
DRAGOn: Designing RAG On Periodically Updated Corpus
Fedor Chernogorskii, Sergei Averkiev, Liliya Kudraleeva +4
This paper introduces DRAGOn, method to design a RAG benchmark on a regularly updated corpus. It features recent reference datasets, a question generation framework, an automatic e…
CIDRe: A Reference-Free Multi-Aspect Criterion for Code Comment Quality Measurement
Maria Dziuba, Valentin Malykh
Effective generation of structured code comments requires robust quality metrics for dataset curation, yet existing approaches (SIDE, MIDQ, STASIS) suffer from limited code-comment…
StRuCom: A Novel Dataset of Structured Code Comments in Russian
Maria Dziuba, Valentin Malykh
Structured code comments in docstring format are essential for code comprehension and maintenance, but existing machine learning models for their generation perform poorly for Russ…
ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization
Dmitriy Shopkhoev, Ammar Ali, Magauiya Zhussip +4
We introduce ReplaceMe, a generalized training-free depth pruning method that effectively replaces transformer blocks with a linear operation, while maintaining high performance fo…
Iterative Self-Training for Code Generation via Reinforced Re-Ranking
Nikita Sorokin, Ivan Sedykh, Valentin Malykh
Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code gene…
Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair
Maksim Borisov, Zhanibek Kozhirbayev, Valentin Malykh
Machine translation for low resource language pairs is a challenging task. This task could become extremely difficult once a speaker uses code switching. We propose a method to bui…
SumHiS: Extractive Summarization Exploiting Hidden Structure
Tikhonov Pavel, Anastasiya Ianina, Valentin Malykh
Extractive summarization is a task of highlighting the most important parts of the text. We introduce a new approach to extractive summarization task using hidden clustering struct…
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski +3
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the model…
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov, Hai Le, Prateek Rajput +4
Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper,…
DetIE: Multilingual Open Information Extraction Inspired by Object Detection
Michael Vasilkovsky, Anton Alekseev, Valentin Malykh +6
State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order no…
How not to Lie with a Benchmark: Rearranging NLP Leaderboards
Shavrina Tatiana, Malykh Valentin
Comparison with a human is an essential requirement for a benchmark for it to be a reliable measurement of model capabilities. Nevertheless, the methods for model comparison could…