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.

The paper reports on the first steps in developing a time-stamped multimodal dataset of reading data by Bulgarian children. Data are being collected, structured and analysed by means of ReadLet, an innovative infrastructure for multimodal language data collection that uses a tablet as a reader’s front-end. The overall goal of the project is to quantitatively analyse the reading skills of a sample of early Bulgarian readers collected over a two-year period, and compare them with the reading data of early readers of Italian, collected using the same protocol. We illustrate design issues of the experimental protocol, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of the Bulgarian dataset for reading research, we present some preliminary statistical analyses of our recently collected data. They show robust convergence trends between Bulgarian and Italian early reading development stages.

The paper presents an effort on transferability of noun–verb and noun–adjective derivative and semantic relations to noun-noun relations. The approach relies on information from semantic classes and existing inter-POS derivative and (morpho)semantic relations between noun and verb, and noun and adjective synsets. We have added semantic relations between nouns in WordNet that are indirectly linked via verbs and adjectives. Observations on the combination between the relations and semantic classes of nouns they link, may facilitate further efforts in assigning semantic properties to nouns pointing to their abilities to participate in predicate-argument structures.

The paper discusses the enrichment of WordNet data through merging of WordNet concepts and Corpus Pattern Analysis (CPA) semantic types. The 253 CPA semantic types are mapped to the respective WordNet concepts. As a result of mapping, the hyponyms of a synset to which a CPA semantic type is mapped inherit not only the respective WordNet semantic primitive but also the CPA semantic type.