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PaperPersiChat: Scientific Paper Discussion Chatbot using Transformers and Discourse Flow Management
P. 584–587.
Chernyavskiy A., Bregeda M., Nikiforova M.
The rate of scientific publications is increasing exponentially, necessitating a significant investment of time in order to read and comprehend the most important articles. While ancillary services exist to facilitate this process, they are typically closed-model and paid services or have limited capabilities. In this paper, we present PaperPersiChat, an open chatbot-system designed for the discussion of scientific papers. This system supports summarization and question-answering modes within a single end-to-end chatbot pipeline, which is guided by discourse analysis. To expedite the development of similar systems, we also release the gathered dataset, which has no publicly available analogues.
In book
Association for Computational Linguistics, 2023.
Yusupov V., Sukhorukov N., Frolov E., User Modelling and User-Adapted Interaction 2026 Vol. 36 Article 2
Graph-based recommender systems have emerged as a powerful paradigm for personalized recommendations. However, their reliance on full model retraining to incorporate new users or new interactions creates scalability barriers. The task becomes infeasible in real-life recommender systems due to excessive time and resource costs involved. To address this limitation, we propose a fast and efficient ...
Added: March 15, 2026
Anna Volodkevich, Danil Gusak, Klenitskiy A. et al., User Modelling and User-Adapted Interaction 2025 No. 35 Article 13
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of transformer-based generative models for the Top-K sequential recommendation task, where the goal is to predict items that a user is likely to interact with in the “near future.” This goal aligns with ...
Added: January 26, 2026
Sherman K., Ignatov D. I., Tatiana I. Shishkovskaya et al., , in: Analysis of Images, Social Networks and Texts, 12th International Conference, AIST 2024, Bishkek, Kyrgyzstan, October 17–19, 2024, Revised Selected PapersVol. 15419.: Springer, 2024. P. 94–108.
More than 3% of people worldwide experience depression. This diagnosis is established through interviews and clinical observations, which is a time- and money-demanding process. Additionally, there are a variety of symptoms associated with depression that are difficult to capture due to the limited capabilities of a human being. Many studies propose methods of automatic mental ...
Added: January 23, 2026
Varnavsky A., , in: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2025. Ch. 23 P. 507–511.
Added: November 7, 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
Джейранян А. Д., Plaksin M. A., В кн.: Инженерное образование в цифровом обществе: Материалы Международной научно-методической конференции (Республика Беларусь, Минск, 14 марта 2024 года). В 2 ч. Ч.2Ч. 2.: Мн.: БГУИР, 2024. С. 294–298.
Added: February 17, 2025
Джейранян А. Д., Plaksin M. A., В кн.: Интеллектуальные информационные системы: теория и практика. Сборник научных статей по материалам V Международной конференции (Курск, 19–21 ноября 2024 года).: Курск: Курский государственный университет, 2024. С. 7–12.
The paper describes a method for using generative artificial intelligence to organize group examinations. A set of instructions has been formed that can be used for this purpose. The application of the method in the field of risk management (in programming and economics) is demonstrated. Several popular generative chatbots are compared. The correct application of ...
Added: February 17, 2025
Джейранян А. Д., Plaksin M. A., В кн.: Экономика 5.0: коллективный интеллект и развитие: материалы VIII Пермского экономического конгресса (г.Пермь, ПГНИУ, 1–2 февраля 2024 г.).: Пермь: ПГНИУ, 2024. С. 83–92.
The article evaluates the possibility of using currently publicly available generative artificial intelligence systems to organize group examination of software projects. The formulation of requests to chatbots (instructs, prompts) is proposed, which are designed to ensure that the necessary information is received. ...
Added: February 17, 2025
Dzheiranian A. D., Plaksin M. A., Proceedings of the Institute for System Programming of the RAS 2024 Vol. 36 No. 2 P. 73–82
The article highlights an innovative approach to risk management in software projects using
generative artificial intelligence. It describes a methodology that involves the use of publicly available chatbots
to identify, analyze, and prioritize risks. The Crawford method is used as a basis for risk identification. The
authors propose specific formulations of requests to chatbots (instructs, prompts) that facilitate ...
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
Gorshkov S., Ignatov D. I., Chernysheva A. et al., IEEE Access 2025 Vol. 13 P. 962–979
Identifying potentially high-performing students is crucial for universities aiming to enhance educational outcomes, for companies seeking to recruit top talents early, and for advertising platforms looking to optimize targeted marketing. This paper introduces an algorithm designed to identify students with exceptional academic performance by analyzing their subscriptions to communities on the social network VKontakte. The ...
Added: January 3, 2025
Komashko M. N., Труды по интеллектуальной собственности 2024 Т. 50 № 3 С. 118–128
The paper deals with theory and practice issues related to such type of artificial intelligence as large language models, in particular, ChatGPT. The main attention is paid to spheres of human activity, in which the exchange of information stated in the form of text is of the greatest importance: science, education and journalism (media sphere).
The ...
Added: December 29, 2024
Lyutkin D. A., D. V. Pozdnyakov, Soloviev A. A. et al., Automation and Remote Control, США 2024 Vol. 85 No. 3 P. 297–308
The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation with a focus on expert specialization. By harnessing the capabilities of transformers, we fine-tuned the pretrained RuBERT model on a varied ...
Added: September 26, 2024
Chernyavskiy A., Ostyakova L., Ilvovsky D., , in: Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024).: Association for Computational Linguistics, 2024. P. 149–160.
Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified ...
Added: May 9, 2024
Chernyavskiy A., Ilvovsky D., Nakov P., , in: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: (Volume 1: Long Papers).: Association for Computational Linguistics, 2024. P. 1452–1462.
Added: May 9, 2024
A. Dubelschikov, Tsoy T., Li H. et al., , in: 2023 7th International Conference on Information, Control, and Communication Technologies (ICCT), 2-6 Oct. 2023.: IEEE, 2023. P. 1–3.
This work develops a concept of using stationary IoT cameras with motion tracking and notification functions employed for a teleoperated unmanned aerial vehicles (UAV) control. The notification function uses a popular messenger to improve an operator convenience. This approach allows the operator using a mobile phone as a UAV control panel by notifying on a ...
Added: May 8, 2024
Bogolepova S., Бабасян Е. Р., Преподаватель XXI век 2024 № 1 С. 137–154
Nowadays digital instruments based on artificial intelligence are increasingly used by professionals in different spheres. This study analyses the potential use of a specialised digital platform and a chat-bot for the design of language learning and assessment tasks. We focus both on the wording of close-ended and open-ended tasks, and the language material included in ...
Added: March 29, 2024
Lyutkin D., Soloviev A., Zhukov D. et al., Working papers by Cornell University. Series math "arxiv.org" 2023 P. 1–16
The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation with a focus on expert specialization. By harnessing the capabilities of transformers, we fine-tuned the pre-trained RuBERT model on a varied ...
Added: November 27, 2023
Alexander Chernyavskiy, Ilvovsky D., , in: Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue.: Association for Computational Linguistics, 2023. P. 519–529.
Recent transformer-based approaches to multi-party conversation generation may produce syntactically coherent but discursively inconsistent dialogues in some cases. To address this issue, we propose an approach to integrate a dialogue act planning stage into the end-to-end transformer-based generation pipeline. This approach consists of a transformer fine-tuning procedure based on linearized dialogue representations that include special ...
Added: October 6, 2023