Thesis Proposal: Diagnosing and Mitigating Semantic Interference in Script-Sharing Low-Resource Language Models: A Case Study on Square Bai Script

AbstractMultilingual language models now cover more languages than ever, yet script-sharing low-resource languages remain vulnerable to failures driven by script and dominant-language priors. This dissertation studies one such failure mode, semantic interference, in Square Bai Script, where many forms resemble Chinese characters but often differ in meaning. We argue that current adaptation pipelines underperform not only because Bai is low-resource, but because they treat visible overlap as safe transfer by default. Building on an expert-validated corpus of 28,382 Bai-Chinese sentence pairs, an out-of-domain epigraphic set and a reproducible encoding pipeline, the dissertation will (1) diagnose semantic interference, (2) compare adaptation strategies under realistic compute constraints, and (3) estimate when shared-script transfer helps or harms adaptation. The long-term goal is Bai-capable understanding and generation. The dissertation addresses the prerequisite problem of safe and effective adaptation in a script-sharing low-resource setting.