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Comparative Study of LoRA and Full Fine-Tuning in Large Language Models
P. 90–98.
E.V. Surikova, E.A. Sabidaeva
In book
Челябинск: Издательский центр ЮУрГУ, 2025.
Rezunik L., Alexandrov D., Mikhail Prozorskiy, , in: Intelligent Decision Technologies. Proceedings of the 17th KES-IDT 2025 ConferenceVol. 450.: Springer, 2026. P. 81–91.
Multi-Agent Systems (MAS) can benefit from Large Language Models (LLMs), but hallucinations pose risks to decision-making. This paper introduces an approach for creating MAS based on LLMs and proposes a generalized architecture for such systems. We ensure that reasoning is conducted through predicate logic to minimize errors, and LLMs are exclusively utilized to translate natural ...
Added: September 14, 2026
Buzaev F., Mullakhmetov R., Bogachev R. et al., Association for Computational Linguistics, 2026.
Playlist generation based on textual queries using large language models (LLMs) is becoming an important interaction paradigm for music streaming platforms. User queries span a wide spectrum from highly personalized intent to essentially catalog-style requests. Existing systems typically rely on non-personalized retrieval/ranking or apply a fixed level of preference conditioning to every query, which can ...
Added: June 22, 2026
Stepin A., Mozikov M., Kabanov A. et al., IEEE Access 2026 Vol. 14 P. 48127–48144
The deployment of large language models (LLMs) in interactive roles such as automated negotiators, customer service agents, and strategic partners requires them to handle not only logical tasks but also the socio-emotional dimensions of interaction. In these situations, success often relies on understanding social cues, building trust, and using persuasion effectively. These skills are closely ...
Added: June 16, 2026
Tyuryakova-Matveeva D., Цифровые гуманитарные исследования 2026 № 1 С. 4–26
The article explores cultural references in the works of Andrei Voznesensky by analyzing the personalities he mentions. A total of 1,678 works were processed, including poetry, prose, and early unpublished poems. NER methods based on Natasha, spaCy, and LLM Grok tools made it possible to study the frequency of mentions of famous people and their ...
Added: May 31, 2026
Beknazarov N., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 3–16.
This paper addresses the challenge of efficiently training Large Language Models (LLMs) on large-scale, sparse omics datasets in high-performance computing (HPC) environments. Using over 1000 BED tracks as a representative data source, we propose a method combining interval-based chunked storage, sparse matrix transformation, and parallel data loading, integrated within a PyTorch Lightning training framework. Our ...
Added: May 19, 2026
Vladimir Bogachev, Aletov V., Alexander Molozhavenko et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 20503 P. 1–26.
This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework integrates three key components to achieve this: (1) we derive Riemannion, a new Riemannian optimizer on the fixed-rank ...
Added: April 29, 2026
Seleznyov M., Chaichuk M., Ershov G. et al., , in: Findings of the Association for Computational Linguistics: EMNLP 2025.: Association for Computational Linguistics, 2025. P. 20370–20385.
Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 4 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural ...
Added: February 3, 2026
Ganeeva V., Khrabrov K., Kadurin A. et al., Journal of Cheminformatics 2025 No. 17 Article 164
The recent integration of natural language processing into chemistry has advanced drug discovery. Molecule representations in language models (LMs) are crucial to enhance chemical understanding. We explored the ability of models to match the same chemical structures despite their different representations. Recognizing the same substance in different representations is an important component of emulating the ...
Added: February 3, 2026
Anastasia V. Kolmogorova, Elizaveta R. Kulikova, Vladislav V. Lobanov, Supercomputing Frontiers and Innovations 2025 Vol. 12 No. 3 P. 121–140
Museum reviews provide rich insight into visitor preferences and can drive useful change within institutions, yet they have attracted little attention in sentiment research owing to limited commercial interest and the multi-thematic nature of reviews. In this study we analysed over 12 000 reviews in Russian for 15 museum sites collected from nine different platforms. ...
Added: November 30, 2025
Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov et al., , in: 39th Conference on Neural Information Processing Systems (NeurIPS 2025).: NeurIPS, 2025. P. 94605–94642.
We introduce AutoJudge, a method that accelerates large language model (LLM) inference with task-specific lossy speculative decoding. Instead of matching the original model output distribution token-by-token, we identify the generated tokens that affect the downstream quality of the response, relaxing the distribution match guarantee so that the "unimportant" tokens can be generated faster.Our approach relies ...
Added: November 6, 2025
Iuliia Alekseenko, Dagaev D., Sofiia Paklina et al., Journal of Economic Behavior and Organization 2025 Vol. 240 Article 107330
Added: November 6, 2025
Anton R., Mikhalchuk M., Rahmatullaev T. et al., , in: Findings of the Association for Computational Linguistics: NAACL 2025.: Association for Computational Linguistics, 2025. P. 7757–7764.
We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as minor (e.g., determiners, punctuation) carry surprisingly high context. Notably, removing these tokens — especially stopwords, articles, and commas — consistently degrades performance on MMLU and BABILong-4k, even if removing only irrelevant tokens. Our analysis ...
Added: November 6, 2025
Voevodina E., Современная зарубежная психология 2025 Т. 14 № 3 С. 172–181
Context and relevance. Well-being research faces methodological limitations of conventional psychometric measures, criticized for poor ecological validity, limited information yield, and inadequate capture of multidimensional construct of well-being. Advanced natural language processing (NLP) technologies offer solutions to these constraints. Objective. To evaluate opportunities and challenges of transformer-based NLP for well-being research. Methods and materials. We conducted an analytical review of ...
Added: October 9, 2025
Storchevoy M., Mylnikov L., Чернышев В. В. et al., / SSRN. Серия "Working Papers". 2025.
Внимание к охране природы принимает все большую значимость для бизнеса с одной стороны в связи с ужесточением в природоохранном законодательстве, а с другой в связи с использованием ESG рейтингов при принятии решений о коммерческой деятельности компаний. Составление рейтинга LLM систем, способных оказывать консультационные услуги в области природоохраны и ESG, позволяет осуществить выбор такой системы для ...
Added: September 18, 2025
Хашутогова У. П., Berezner T., Poddiakov A., Новые психологические исследования 2025 № 3 С. 100–125
The rapid advancement of artificial intelligence technologies has drawn increasing attention from psychological researchers. While neural networks are being integrated into nearly all domains of human activity, the boundaries of their applicability remain unclear — particularly regarding the originality and practical value of the content they generate. Proponents advocate for their widespread adoption, whereas skeptics ...
Added: September 4, 2025
Skrynnikova I.V., Вестник Волгоградского государственного университета. Серия 2: Языкознание 2025 Vol. 23 No. 5 P. 99–107
In recent years, numerous studies have pointed to the ability of artificial intelligence (AI) to generate and analyze expressions of natural language. However, the question of whether AI is capable of actually interpreting human language, rather than imitating its understanding, remains open. Metaphors, being an integral part of human language, as both a common figure ...
Added: August 1, 2025
Suleykin, A., Babenko, R., Panfilov, P., , in: Proceedings of the 35th International DAAAM Virtual Symposium ''Intelligent Manufacturing & Automation''Vol. 1.: NY: DAAAM International Vienna, 2024. Ch. 20 P. 0157–0164.
Added: April 5, 2025
Razzhigaev A., Kurkin M., Goncharova E. et al., , in: Proceedings of the 2nd GenBench Workshop on Generalisation (Benchmarking) in NLP.: Association for Computational Linguistics, 2024. P. 183–195.
We introduce OmniDialog — the first trimodal comprehensive benchmark grounded in a knowledge graph (Wikidata) to evaluate the generalization of Large Multimodal Models (LMMs) across three modalities. Our benchmark consists of more than 4,000 dialogues, each averaging 10 turns, all annotated and cross-validated by human experts. The dialogues in our dataset are designed to prevent ...
Added: February 21, 2025
Fenogenova A., Chervyakov, A., Martynov N. et al., , in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2024Vol. 1: Long Papers.: Bangkok: Association for Computational Linguistics, 2024. P. 9920–9948.
Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers’ attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still need to be better understood. To address these issues, ...
Added: February 17, 2025
Razzhigaev A., Mikhalchuk M., Goncharova E. et al., , in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2024Vol. 1: Long Papers.: Bangkok: Association for Computational Linguistics, 2024. P. 5376–5384.
This paper reveals a novel linear characteristic exclusive to transformer decoders, including models like GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering an almost perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decreases when the residual component is removed, due to a consistently low transformer layer output ...
Added: February 17, 2025
Razzhigaev A., Mikhalchuk M., Goncharova E. et al., , in: Findings of the Association for Computational Linguistics: EACL 2024.: Association for Computational Linguistics, 2024. P. 868–874.
Added: February 17, 2025