@inproceedings{yagiz-horasan-2026-beyond,
    title = "Beyond the Token: Correcting the Tokenization Bias in {XAI} via Morphologically-Aligned Projection",
    author = "Yagiz, Muhammet Anil  and
      Horasan, Fahrettin",
    editor = {Oflazer, Kemal  and
      K{\"o}ksal, Abdullatif  and
      Varol, Onur},
    booktitle = "Proceedings of the Second Workshop Natural Language Processing for {T}urkic Languages ({SIGTURK} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.sigturk-1.19/",
    pages = "228--235",
    ISBN = "979-8-89176-370-8",
    abstract = "Current interpretability methods for Large Language Models (LLMs) operate on a fundamental yet flawed assumption: that subword tokens represent independent semantic units. We prove that this assumption creates a fidelity bottleneck in Morphologically Rich Languages (MRLs), where semantic meaning is densely encoded in sub-token morphemes. We term this phenomenon the Tokenization-Morphology Misalignment (TMM). To resolve TMM, we introduce MAFEX (Morpheme-Aligned Faithful Explanations), a theoretically grounded framework that redefines feature attribution as a linear projection from the computational (token) basis to the linguistic (morpheme) basis. We evaluate our method on a diverse suite of Turkish LLMs, including BERTurk, BERTurk-Sentiment, Cosmos-BERT, and Kumru-2B. On our embedded benchmark (N=20), MAFEX achieves an average F1@1 of 91.25{\%} compared to 13.75{\%} for standard token-level baselines (IG, SHAP, DeepLIFT), representing a +77.5{\%} absolute improvement, establishing it as the new standard for faithful multilingual interpretability."
}