Exploring Safety-Utility Trade-Offs in Personalized Language Models

Understand

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.