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This study demonstrates how Large Language Models replicate native speaker biases against non-native Japanese writing, particularly in assessing fluency and social solidarity within high-stakes contexts like hiring. While the models mirror human language attitudes with some attenuation, they also introduce distinct patterns by differentiating among learner backgrounds where humans do not. These findings are critical for auditing AI evaluation systems to prevent systemic discrimination against L2 speakers in professional environments.
Human-LLM Alignment in Language Attitudes Toward Non-Native Japanese
arXiv:2608.01629v1 Announce Type: new Abstract: 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. Japanese raters rated L2 texts significantly lower on all three dimensions, with a fluency gap roughly twice the size of the status and solidarity gaps. Six LLM judges reproduced the direction of this bias, and five reproduced its ordering across dimensions. The models diverged from humans in two ways: all understated the solidarity gap, the most socially grounded dimension, and all differentiated among learner L1 backgrounds where humans did not. LLM judges thus reproduce native speakers' language attitudes in a structured yet attenuated form, and the language attitudes framework offers a ready-made yardstick for auditing them beyond English.
Abstract: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. Japanese raters rated L2 texts significantly lower on all three dimensions, with a fluency gap roughly twice the size of the status and solidarity gaps. Six LLM judges reproduced the direction of this bias, and five reproduced its ordering across dimensions. The models diverged from humans in two ways: all understated the solidarity gap, the most socially grounded dimension, and all differentiated among learner L1 backgrounds where humans did not. LLM judges thus reproduce native speakers' language attitudes in a structured yet attenuated form, and the language attitudes framework offers a ready-made yardstick for auditing them beyond English.
Computation and Language (cs.CL)
arXiv:2608.01629 [cs.CL]
(or arXiv:2608.01629v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.01629 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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