The automatic generation of queries to a search engine based on the incoming text is important for question-answering, recommendation, and text reuse detection systems. Every such query requires resources from a user and a search engine itself. A method of ensembling query generation models that maximizes the search completeness metric for the minimum number of queries could be useful. The task of selecting the best model or an ensemble of models is trivial for the case of a fixed search engine. However, real search engines are constantly changing their behavior, learning on incoming data, changing their index of web pages and documents. They are black boxes for a user. In this paper we propose an approach to ensemble query generation models based on reinforcement learning. By reformulating the problem so that the agent selects a sequence of models rather than a single query generation model, we guarantee maximum retrieval recall even when the worst possible action is selected. As a reward, we introduce a discount recall metric that penalizes the agent for each extra step of a model request. We modify the UCB learning algorithm so that the re-initialization of the recidivism penalty matrix occurs independently of the engine index state. In this way, we ensure that the top 3 best actions (i.e. sequences of generation model requests) are found in just 5 epochs, each epoch contains 1050 documents. The model ensemble maintains a stable performance even when the index alters in a way that the ensemble was not informed about.

Численным моделированием продемонстрирован эффект теплоизоляции термоядерного топлива при имплозии топливных полостей в протяженной оболочке из тяжелых элементов. Найдены конечные условия имплозии, достижимые с учетом технологических ограничений.

Currently, more efforts are being made to improve the capabilities of Large Language Models than to address their implications. Modern language models are capable of generating texts that appear indistinguishable from those written by human experts. While providing a high quality of life, such breakthroughs at the same time pose new challenges in education, science and social media. In addition, existing approaches to detect texts created by artificial intelligence either require high computational cost or access to the internal computation of LLMs, which in turn hinders their public availability. Based on these considerations, this paper presents a new paradigm for detecting texts created by artificial intelligence based on on collecting preliminary token statistics and computing n-gram perplexity features. On the combination of HC3, M4GT and MAGE datasets it shows a speedup of 2x over existing approaches with a quality drop around 5%. Moreover, the combination of methods achieves the best quality. This strikes a balance between computational cost, accessibility and performance.

A wide class of natural language processing tasks is solved using markup. At the moment, the vast majority of models and datasets rely on a simple markup structure containing only fragments and labels. Moreover, simple classification metrics such as F1, Precision, Recall are used to evaluate the model’s accuracy. The problem with such metrics is that they do not take into account all aspects of the markup structure and that they are applicable only under the assumption of the existence of an ideal markup. This paper proposes a more general and universal markup structure that allows solving complex problems and builds a methodology for multi-criteria evaluation of text markup models based on inconsistent expert markup. After that, the application of the constructed method is considered to assess the quality of the model obtained within the winning algorithm of the “READ//ABLE” competition, which focused on building an effective essay markup system. The results demonstrate that the new markup structure and evaluation approach provides a more comprehensive and accurate assessment of model performance, addressing the limitations of traditional metrics by accounting for complex markup scenarios and expert inconsistencies.