Human-LLM Alignment in Language Attitudes Toward Non-Native Japanese
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring and academic assessment, putting non-native speakers at particular risk. Drawing on the language attitudes framework, we compared human and LLM evaluations of parallel L1- and L2-written Japanese emails on three dimensions: fluency, status, and solidarity.
PragAlign is a reply‑assistance system that separates context reading from selective clarification, designed for multilingual settings. In evaluations with native Chinese and Japanese speakers, PragAlign achieved better rankings than baseline methods in Chinese and the highest top‑rank rate in Japanese, while both groups often chose the same top condition. The study highlights both shared and language‑specific judgment patterns that can inform culturally aware reply‑assistance tools.
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