Characterizing Bias: Benchmarking Large Language Models in Simplified
  versus Traditional Chinese
Authors: name: Hanjia Lyu, name: Jiebo Luo, name: Jian Kang, name: Allison Koenecke

While the capabilities of Large Language Models (LLMs) have been studied in
both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit
differential performance when prompted in these two variants of written
Chinese. This understanding is critical, as disparities in the quality of LLM
responses can perpetuate representational harms by ignoring the different
cultural contexts underlying Simplified versus Traditional Chinese, and can
exacerbate downstream harms in LLM-facilitated decision-making in domains such
as education or hiring. To investigate potential LLM performance disparities,
we design two benchmark tasks that reflect real-world scenarios: regional term
choice (prompting the LLM to name a described item which is referred to
differently in Mainland China and Taiwan), and regional name choice (prompting
the LLM to choose who to hire from a list of names in both Simplified and
Traditional Chinese). For both tasks, we audit the performance of 11 leading
commercial LLM services and open-sourced models -- spanning those primarily
trained on English, Simplified Chinese, or Traditional Chinese. Our analyses
indicate that biases in LLM responses are dependent on both the task and
prompting language: while most LLMs disproportionately favored Simplified
Chinese responses in the regional term choice task, they surprisingly favored
Traditional Chinese names in the regional name choice task. We find that these
disparities may arise from differences in training data representation, written
character preferences, and tokenization of Simplified and Traditional Chinese.
These findings highlight the need for further analysis of LLM biases; as such,
we provide an open-sourced benchmark dataset to foster reproducible evaluations
of future LLM behavior across Chinese language variants
(https://github.com/brucelyu17/SC-TC-Bench).