Meta Kokalj

Increasingly advanced technological tools are becoming progressively integrated into the lives of individuals and society. The process of integration can even go to the extent, that technology merges with its user which can result in the creation of new types of entities. In this paper two examples of cognitive technologies are presented (cognitive biometrics and brain-computer interfaces) in the attempt to highlight some of the ethical, legal and practical dilemmas which arise from mentioned technologies in their use. Increasing accessibility and expansion of their usage will inevitably increase the potential for originally unforeseen problems and products of these technologies. Therefore, comprehensive measures in the form of a deliberated legal solutions, which will anticipate suitable safeguard against the unlawful and unethical use of cognitive technologies in social practice, should be employed without undue delay. Življenje posameznikov in družbe kot celote je čedalje bolj prežeto z vse bolj naprednimi tehnološkimi orodji, včasih celo do take mere, da prihaja do spajanja človeško organskih in strojnih sistemov in ustvarjanja novih tipov entitet. V prispevku sta predstavljena dva primera uvajanja kognitivnih tehnologij (kognitivna biometrija in možganski vmesniki) in poskus izpostavitve nekaterih etičnih, pravnih in praktičnih dilem, ki izhajajo iz njihove uporabe. S tehnološkimi zmožnostmi se neizogibno širi ponudba izvorno nepredvidenih (a v dobi velikega podatkovja ne neuporabnih) uporabniških produktov omenjenih tehnologij, iz njihove uporabe pa vznikajo vedno novi načini poseganj v človekovo osebnost. Vse to zahteva hiter odziv v obliki celovite pravne ureditve problematike, ki bo na domišljen način vgradila ustrezna varovala pred nezakonito in neetično uporabo kognitivnih tehnologij v družbeni praksi.

This paper presents a novel methodology for constructing a diverse and comprehensive paragraph-level dataset tailored for Natural Language Inference tasks. By incorporating five scenario categories-historical events, scientific explanations, everyday situations, news reports, and fictional stories-our approach ensures broad coverage of various reasoning patterns, including transitive reasoning. The methodology employs iterative and Chain-of-Thought prompting to elicit detailed, context-rich outcomes by generating intermediate reasoning steps. Our dataset is notable for adding detailed explanations for each hypothesis, providing a valuable overview of the thought process behind each categorization. To assess the quality of these outputs, we implemented the OQACM metrics, a comprehensive framework for evaluating large language model outputs across fluency, coherence, and inference quality. This approach improves the interpretability of language models and offers detailed insights into their inferential processes, advancing NLI dataset development.