Assessing the broad general knowledge of Large Language Models (LLMs) across multiple domains in Bulgarian remains challenging due to the limited availability of Bulgarian evaluation benchmarks. To address this gap, we introduce the Bulgarian Massive Multitask Language Understanding benchmark (MMLU-BG), designed to evaluate whether LLMs possess generalised knowledge capabilities beyond simple text prediction in Bulgarian. This paper presents the structure, the development protocol, and the size of the MMLU-BG benchmark. It is tested in comparison with the original MMLU for English across seven LLMs selected according to specific criteria. The experiments demonstrate that the MMLU-BG benchmark assesses multi-domain versatility and highlights the models’ strengths and weaknesses across different subject areas.

Hydra is a Wordnet management system where the Synsets from different languages live in a common relational structure (Kripke frame) with a user-frendly GUI for searching, editing and alignment of the objects from the different languages. The data is retrieved by means of a modal logic query language. Despite its many merits the system stores only the current state of the wordnet data. Wordnet editing and development opens questions for wordnet data, structure and its consistency over time. The new Time Flow Hydra uses a Dynamic wordnet model with a discrete time embeded where all the states of all the objects are stored and accessed simultaneously. This provides the ability to track the changes, to detect the desired and undesired results of the data evolution. For example, we can ask which objects 10 days ago had 2 hyponyms, and 5 days later have 3.