?
Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option
P. 5967–5974.
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and second, it often makes mistakes in whether to use a tool at all. We introduce Toolken+ that mitigates the first problem by reranking top-k tools selected by ToolkenGPT and the second problem with a special REJECT option such that the model will generate a vocabulary token if REJECT is ranked first. We demonstrate the effectiveness of Toolken+ on multistep numerical reasoning and tool selection tasks.
In book
Association for Computational Linguistics, 2024.
Silakov D., Системный администратор 2026 С. 84–89
Social media users rarely think about what lies behind the beautiful facade of activity feeds, teeming with photos and video stories. However, the widespread popularity of such platforms generates a huge amount of all sorts of content that needs to be stored, processed quickly, and displayed, and in the era of AI, it also needs ...
Added: September 28, 2026
Куделя А. В., Алшауи Р., Shirnin A., , in: Proceedings of the 20th International Workshop on Semantic Evaluation (2026).: Association for Computational Linguistics, 2026. P. 2347–2353.
In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM),our submission to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning. Evaluating narrative similarity is a profound computational challenge that requires models to look past concrete, superficial elements such as specific names, actors, objects, or settings to isolate and compareabstract patterns of ...
Added: September 28, 2026
Алшауи Р., Raj A., Куделя А. В. et al., , in: Proceedings of the 20th International Workshop on Semantic Evaluation (2026).: Association for Computational Linguistics, 2026. P. 2648–2656.
This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment “valence” (positivity) and “arousal” (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full ...
Added: September 28, 2026
Association for Computational Linguistics, 2026.
Added: September 28, 2026
Springer, 2026.
Two volumes of the SPECOM 2026 proceedings contain a collection of submitted papers presented at SPECOM 2026, which were thoroughly reviewed by members of the Program Committee and additional reviewers consisting of almost 80 experts in the conference topic areas. In total, 65 regular full papers out of 99 submissions made via the EasyChair electronic ...
Added: September 20, 2026
Ravedovskaya U., Didenko A., Филатова А. А., Journal of Teaching English for Specific and Academic Purposes 2025 Vol. 13 No. 3 P. 529–538
Emergence of generative artificial intelligence (GenAI) is revolutionizing teaching and evaluation in higher education. Early adopters have already demonstrated how large language model (LLM) conversational agents can serve as on-demand tutors, yet empirical evidence regarding their effectiveness in facilitating conceptual learning in non-computational domains is scant. Based on constructivist learning theory, this paper presents a ...
Added: September 18, 2026
M.: Max press, 2026.
The volume includes 64 papers from the international conference on computational linguistics and intelligent technologies 'Dialogue 2026,' representing a broad spectrum of theoretical and applied research in the field of natural language description, language process modeling, and the development of practically applicable computational linguistic technologies.
For specialists in theoretical and applied linguistics and intelligent technologies. ...
Added: June 27, 2026
Cherednichenko O., Herbert A., Poptsova M., Computational and Structural Biotechnology Journal 2025 Vol. 27 P. 992–1000
Large language models (LLMs) in genomics have successfully predicted various functional genomic elements. While their performance is typically evaluated using genomic benchmark datasets, it remains unclear which LLM is best suited for specific downstream tasks, particularly for generating whole-genome annotations. Current LLMs in genomics fall into three main categories: transformer-based models, long convolution-based models, and state-space models ...
Added: June 19, 2026
Severin N., Kartushov D., Urzhumov V. et al., , in: Advances in Information Retrieval: 48th European Conference on Information Retrieval, ECIR 2026, Delft, The Netherlands, March 29 – April 2, 2026, Proceedings, Part II. (LNCS, volume 16484).: Cham: Springer Publishing Company, 2026. P. 508–517.
Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in cap-turing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches cre-ate prohibitive inference costs in real time. To address these limitations, we present a ...
Added: June 18, 2026
Abdullaeva I., Karpukhin I., Filatov A. et al., IEEE Access 2026 Vol. 14 P. 59390–59408
Event sequences, a specialized type of tabular data annotated with timestamps, are prevalent across practical domains such as finance, retail, social networks, and healthcare. Despite the importance of event sequence modeling and analysis, there has been little effort to adapt Large Language Models (LLMs) to this domain. In this paper, we propose a novel solution ...
Added: June 16, 2026
Association for Computational Linguistics, 2024.
Added: June 14, 2026
Association for Computational Linguistics, 2026.
Added: June 13, 2026
JOURNAL OF EDUCATIONAL TECHNOLOGY DEVELOPMENT AND EXCHANGE 2026 Vol. 19 No. 2 P. 141–169
Educational assessments, from low-stakes classroom tests to high-stakes national examinations, require item pools that are valid, fair, and secure. Automated Item Generation (AIG) aims to efficiently produce large pools of calibrated test items. This paper adopts a two-part design: (1) a brief historical mapping situating LLM-based AIG within the broader AIG trajectory; and (2) a ...
Added: May 5, 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
Сулейкин А. С., Сорокина В., Пятецкий В. Е., , in: 2025 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency.: [б.и.], 2025. P. 748–753.
Effective metadata management is fundamental to data governance, ensuring that data assets are discoverable, understandable, and usable across the enterprise. However, traditional metadata systems often remain purely technical, describing structures without conveying business meaning. This disconnect — known as the semantic gap — limits the interpretability and value of metadata for business users. To address ...
Added: April 17, 2026
Behzadidoost R., Neurocomputing 2025 Vol. 665 P. 1–21
While earlier research has focused on detecting misinformation content, identifying the users who spread it, referred to in this paper as fake information spreaders, remains a relatively new challenge. These users deliberately mix true and false information, making detection more difficult. This paper proposes a textual fingerprint learning model to detect fake information spreaders. The ...
Added: March 12, 2026
Karpov I., Kirillovich A., Goncharova E. et al., Plos One 2026 Vol. 21 No. 1 Article e0339468
Large language models (LLMs) offer significant potential for constructing commonsense knowledge graphs from text, demonstrating adaptability across diverse domains. However, their effectiveness varies significantly with domain-specific language, highlighting a critical need for specialized benchmarks to assess and optimize knowledge graph construction sub-tasks like named entity recognition, relation extraction, and entity linking. Currently, domain-specific benchmarks are ...
Added: January 15, 2026
Karavaeva E., Vasilevsky V., Ланин Г. М. et al., Труды Института системного программирования РАН 2025 Т. 37 № 4 С. 175–190
The ongoing digitalization of education requires new ways of presenting information and attention retention mechanisms. The aim of the presented work is to propose a solution for implementing a large language model, which will interactively generate prompts of different types, within an e-learning course on programming. The main approaches are the analysis of existing relatively ...
Added: December 25, 2025
David Arteaga, Poptsova M., Computational and Structural Biotechnology Journal 2026 Vol. 31 P. 82–93
Accurate predictions and large-scale identification of protein-protein interactions (PPIs) are crucial for understanding their inherent biological mechanisms and protein functions in virtually all biological processes. Nowadays, graph-based deep learning models have made significant contributions in modeling proteins with physicochemical and geometric features. However, most of these models rely on conventional graph construction methods, such as ...
Added: December 22, 2025
Washington, United States of America: AAAI Press, 2025.
AAAI-25 Technical Tracks 23 (Natural Language Processing II) collects peer-reviewed research papers that advance the state of natural language processing, with an emphasis on large language models, efficient inference, instruction following, retrieval augmentation, and multimodal language understanding. The papers address both theoretical and practical challenges, including model efficiency, interactive generation, grounding in external knowledge and ...
Added: December 18, 2025
Kudelya A., Shirnin A., , in: Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025).: Association for Computational Linguistics, 2025. P. 1528–1533.
This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning - removing specific knowledge from a large language model without retraining from scratch and compromising its overall utility (SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models). The algorithm combines classical influence functions to remove the influence of ...
Added: November 17, 2025
Morozov L., Mogilevskii A., Shirnin A., , in: Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025).: Association for Computational Linguistics, 2025. P. 2000–2007.
This paper describes LIBU (LoRA enhanced influence-based unlearning), an algorithm to solve the task of unlearning - removing specific knowledge from a large language model without retraining from scratch and compromising its overall utility (SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models). The algorithm combines classical influence functions to remove the influence of ...
Added: November 17, 2025