Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

The paper investigates why large reasoning language models struggle to transfer parametric knowledge across different scripts. Through observational data and regression analysis on ECLeKTic and MultiLoKo datasets, the authors find that script mismatch—not language family—is the main predictor of transfer failure when controlling for model capability and question difficulty. By providing key entities in the source language and training models to reason about transliteration ambiguities, they demonstrate a reduction in the cross‑script transfer gap, suggesting that post‑training improvements can enhance cross‑lingual knowledge transfer.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• fine-tuning