Beyond the Token: Correcting the Tokenization Bias in XAI via Morphologically-Aligned Projection
Year: 2026
Type: conference-paper
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.
Authors: Muhammet Anil Yagiz, Fahrettin Horasan
Institution: Kırıkkale University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140134996