Register Bias in Complexity-Based Large Language Model Routing

Research indicates that large language model (LLM) routing systems, which direct queries to models of varying capabilities based on estimated complexity, exhibit register bias. Queries written in non-standard English registers, such as African American English or English from second-language writers, are systematically routed to lower-capacity models compared to equivalent queries in standard English. This bias is primarily driven by input length, as non-standard registers often omit function words, making them appear shorter and thus simpler to routing algorithms.

This research reveals a critical operational bias in widely adopted large language model routing mechanisms, impacting the equitable access to advanced AI capabilities. Organizations leveraging such systems must understand that their current configurations may inadvertently disadvantage certain user groups, leading to disparities in service quality and potentially undermining trust and inclusion efforts.

LLM services commonly route queries based on perceived complexity to optimize resource allocation.

Forward consideration, not a verified fact.