You've been too busy 🀄izing bias in other contexts!

Many thanks to the researchers who have inspired our work!! (14/14) @valentinhofmann.bsky.social @jurafsky.bsky.social @haldaume3.bsky.social @hannawallach.bsky.social @jennwv.bsky.social @diyiyang.bsky.social and many others not yet on Bluesky!

We encourage practitioners to use our dataset (github.com/brucelyu17/S...) to audit for biases before choosing an LLM to use, and developers to investigate diversifying training data and research tokenization differences across Chinese variants. (13/14)

This is likely due to differences in tokenization between Simplified Chinese and Traditional Chinese. The exact same names, when translated between language settings, result in significantly different numbers of tokens when represented in each of the models. (12/14)

But, written character choice (in Traditional or Simplified) seems to be the primary driver of LLM preferences. Conditioning on the same names (which have different characters in Traditional vs. Simplified), we can flip our results & get majority Simplified names selected (11/14)

(3) Some LLMs prefer certain characters, like 俊 and 宇, which are more common in Taiwanese names. Baichuan-2 often describes selected Taiwanese names as having qualities related to “talent” and “wisdom.” This does seem like a partial explanation! (10/14)

(2) Gender bias exists: male names are selected more frequently than female names in almost all LLMs. But, balancing our experiments on gender still yields a slight preference for Taiwanese names. (9/14)

(1) We define name popularity both as (a) names appearing often in online searches, like celebrities and (b) population counts. Controlling for either definition doesn’t affect LLM preference for Taiwanese names. (8/14)

Why are we seeing this preference for Taiwanese names among LLMs? We use process of elimination on 4 likely explanations: popularity, gender, character, and written script. (7/14)

Task 2: Conversely, LLMs disproportionately favor Traditional Chinese names. This trend holds regardless of LLM degree of adherence to prompt instructions (with some LLMs refusing to choose a candidate without sufficient info–good!, and some always returning a name) (6/14)