Arab cultural entities that double as everyday Arabic words are harder for language models to recognize, especially when tokenized as single tokens.
Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases
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abstract
Through the use of first name substitution experiments, prior research has demonstrated the tendency of social commonsense reasoning models to systematically exhibit social biases along the dimensions of race, ethnicity, and gender (An et al., 2023). Demographic attributes of first names, however, are strongly correlated with corpus frequency and tokenization length, which may influence model behavior independent of or in addition to demographic factors. In this paper, we conduct a new series of first name substitution experiments that measures the influence of these factors while controlling for the others. We find that demographic attributes of a name (race, ethnicity, and gender) and name tokenization length are both factors that systematically affect the behavior of social commonsense reasoning models.
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On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena
Arab cultural entities that double as everyday Arabic words are harder for language models to recognize, especially when tokenized as single tokens.