This paper presents a study of the linguistic knowledge and generalization capabilities of Large Language Models (LLMs), focusing ontheir morphosyntactic competence. We design three diagnostic tasks: (i) labeling syntactic information at the sentence level - identifying subjects, objects, and indirect objects; (ii) derivational decomposition at the word level - identifying morpheme boundaries and labeling thedecomposed sequence; and (iii) in-depth study of morphological decomposition in German and Amharic. We evaluate prompting strategies in GPT-4o and LLaMA 3.3-70B to extract different types of linguistic structure for typologically diverse languages. Our results showthat GPT-4o consistently outperforms LLaMA in all tasks; however, both models exhibit limitations and show little evidence of abstract morphological rule learning. Importantly, we show strong evidence that the models fail to learn underlying morphological structures. Therefore,raising important doubts about their ability to generalize.

The role of subword segmentation in relation to capturing morphological patterns in LLMs is currently not well explored. Ideally, one would train models like GPT using various segmentations and evaluate how well word meanings are captured. Since this is not computationally feasible, we group words according to their segmentation properties and compare how well a model can solve a linguistic task for these groups. We study two criteria: (i) adherence to morpheme boundaries and (ii) the segmentation consistency of the different inflected forms of a lemma. We select word forms with high and low values for these criteria and carry out experiments on GPT-4o’s ability to capture verbal inflection for 10 languages. Our results indicate that in particular the criterion of segmentation consistency can help to predict the model’s ability to recognize and generate the lemma from an inflected form, providing evidence that subword segmentation is relevant.