Music is a universal cultural practice that influences emotion, ritual and creativity, and it is now represented in many digital modalities: audio recordings, symbolic encodings (MIDI, MusicXML, ABC), visual scores and lyrics. Multimodal Large Language Models (MLLMs) have the ambition to process "everything", including music, and therefore promise to support musical analysis, creation and education. Despite this promise, systematic methods for evaluating whether a MLLM understands music are missing. Existing music-focused benchmarks are fragmented, largely single-modality, Western-centric, and often do not require actual perception of the musical content; methodological details such as prompt design and answer-extraction are frequently omitted or not discussed, and some evaluations rely on proprietary LLMs, hindering reproducibility and raising concerns about test-data leakage. To fill this gap, this dissertation proposes to design a musically multimodal benchmark built on a transparent, fully open evaluation pipeline. The benchmark will present closed-question-answer items across four musical modalities, employ carefully engineered distractor options to enforce genuine perceptual engagement, and follow rigorously documented prompt-selection and answer-extraction procedures. It will further incorporate culturally diverse musical material beyond the dominant Western canon. Guided by three research questions: (1) how to devise robust, reproducible evaluation procedures, (2) how current MLLMs perform across modalities, and (3) how model scores relate to human musical abilities; the benchmark will enable precise diagnosis of model limitations, inform the development of more musically aware AI systems, and provide a principled basis for assessing practical usefulness to musicians and other stakeholders in the creative industry.

We focus on morphological inflection in out-of-vocabulary (OOV) conditions, an under-researched subtask in which state-of-the-art systems usually are less effective. We developed three systems: a retrograde model and two sequence-to-sequence (seq2seq) models based on LSTM and Transformer. For testing in OOV conditions, we automatically extracted a large dataset of nouns in the morphologically rich Czech language, with lemma-disjoint data splits, and we further manually annotated a real-world OOV dataset of neologisms. In the standard OOV conditions, Transformer achieves the best results, with increasing performance in ensemble with LSTM, the retrograde model and SIGMORPHON baselines. On the real-world OOV dataset of neologisms, the retrograde model outperforms all neural models. Finally, our seq2seq models achieve state-of-the-art results in 9 out of 16 languages from SIGMORPHON 2022 shared task data in the OOV evaluation (feature overlap) in the large data condition. We release the Czech OOV Inflection Dataset for rigorous evaluation in OOV conditions. Further, we release the inflection system with the seq2seq models as a ready-to-use Python library.