Is it indeed bigger better? The comprehensive study of claim detection LMs applied for disinformation tackling

This study compares the performance of (1) fine-tuned models and (2) extremely large language models on the task of check-worthy claim detection. For the purpose of the comparison we composed a multilingual and multi-topical dataset comprising texts of various sources and styles. Building on this, we performed a benchmark analysis to determine the most general multilingual and multi-topical claim detector. We chose three state-of-the-art models in the check-worthy claim detection task and fine-tuned them. Furthermore, we selected three state-of-the-art extremely large language models without any fine-tuning. We made modifications to the models to adapt them for multilingual settings and through extensive experimentation and evaluation. We assessed the performance of all the models in terms of accuracy, recall, and F1-score in in-domain and cross-domain scenarios. Our results demonstrate that despite the technological progress in the area of natural language processing, the models fine-tuned for the task of check-worthy claim detection still outperform the zero-shot approaches in a cross-domain settings.Comment: 27 pages, 10 figure

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Voice Command Recognition in Multirobot Systems: Information Fusion

Recent Multirobot systems (MRS) are moving from theoretical considerations and from development and research centres to the area of practical applications. The solutions to real practical problems bring new challenges which are derived from actual requirements, while they are also interesting from theoretical point of view. One of the interesting areas of investigation is the problem of distributed data processing by limited computing and communication performance of individual components in MRS. In this article, the authors try to demonstrate, using a simple example, the possibilities of distributed solution of classification tasks. Such questions as: – To what extent it is appropriate to distribute the tasks among individual elements of the system and to what extent to minimalize the requests on the communication subsystem? – Is it more appropriate, in the design concept of distributed data processing, to use a data fusion system, features fusion or decision fusion? are not universally solvable. Therefore, we refrain from the analytical analysis and the choice of appropriate level of information fusion, but in four different scenarios we focus on solving ‘voice command recognition’ – we would like to show the advantages and disadvantages of individual approaches. The experiments described and the results achieved are based on simulation experiments and verified by experimental and demonstrative MRS