PACUTE: Phonology-, Affix-, and Character-level Understanding of Tokens for Filipino

Abstract:Large language models (LLMs) process text as sequences of subword tokens, which can obscure the character-level and morphological structure that underlies word formation. This limitation is most acute for languages with non-concatenative morphology, where standard tokenizers systematically misalign token boundaries with morpheme boundaries. We introduce PACUTE, a diagnostic benchmark of 4,600 tasks designed to evaluate morphological understanding in Filipino, a language characterized by productive infixation, reduplication, and diacritic-driven lexical distinctions that are typically absent from written text. PACUTE includes a hierarchical diagnostic framework of six compositional levels that localizes where morphological understanding breaks down. Evaluating open-weight LLMs and frontier commercial models, we find that open-weight models perform near chance on morpheme decomposition regardless of scale. Frontier models perform much better, often recovering individual affixes under contains-match scoring, but remain far below their character-level ceilings on compositional tasks of morpheme transformations and syllabification. These results identify productive morphological composition, rather than character access alone, as the persistent bottleneck for Filipino word-structure understanding.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2606.15144 [cs.CL]

(or arXiv:2606.15144v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.15144 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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