Exploring Safety-Utility Trade-Offs in Personalized Language Models

Abstract

As large language models (LLMs) become increasingly integrated into daily applications, it is essential to ensure they function 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. 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-3.1 and Mistral to API-based ones like GPT-3.5 and GPT-4o, exhibit significant variance in performance in terms of safety and utility when personalized with different user identities. Finally, we discuss several strategies to mitigate personalization bias and investigate the origin of personalization bias.