Characterizing Bias: Benchmarking Large Language Models in Simplified
  versus Traditional Chinese
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).
6837dbed1233747046da00f5
Hanjia Lyu
claimed_verified
2025-05-29T20:19:00.877Z
64c939307dba66c3a7e4d215
/avatars/0b662cc1799525188476f3e6e1f97d29.svg
BruceLyu
user
brucelyu
6837dbed1233747046da00f6
Jiebo Luo
6837dbed1233747046da00f7
Jian Kang
6837dbed1233747046da00f8
Allison Koenecke
Characterizing Bias: Benchmarking Large Language Models in Simplified
  versus Traditional Chinese
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).
Research examines LLM performance biases between Simplified and Traditional Chinese in regional term and name choice tasks, attributing differences to training data and tokenization.
Large Language Models LLMs LLM-facilitated decision-making regional term choice regional name choice open-sourced benchmark dataset SC-TC-Bench