  As large language models (LLMs) become increasingly integrated into daily
applications, it is essential to ensure they operate fairly across diverse user
demographics. In this work, we show that LLMs suffer from personalization bias,
where their performance is impacted when they are personalized to a user's
identity. We quantify personalization bias by evaluating the performance of
LLMs along two axes - safety and utility. We measure safety by examining how
benign LLM responses are to unsafe prompts with and without personalization. We
measure utility by evaluating the LLM's performance on various tasks, including
general knowledge, mathematical abilities, programming, and reasoning skills.
We find that various LLMs, ranging from open-source models like Llama (Touvron
et al., 2023) and Mistral (Jiang et al., 2023) to API-based ones like GPT-3.5
and GPT-4o (Ouyang et al., 2022), exhibit significant variance in performance
in terms of safety-utility trade-offs depending on the user's identity.
Finally, we discuss several strategies to mitigate personalization bias using
preference tuning and prompt-based defenses.
