Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation (2404.05970v1)

Abstract: This paper studies retrieval-augmented approaches for personalizing LLMs, which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to LLMs for the purpose of personalized generation. We develop two optimization algorithms that solicit feedback from the downstream personalized generation tasks for retrieval optimization -- one based on reinforcement learning whose reward function is defined using any arbitrary metric for personalized generation and another based on knowledge distillation from the downstream LLM to the retrieval model. This paper also introduces a pre- and post-generation retriever selection model that decides what retriever to choose for each LLM input. Extensive experiments on diverse tasks from the LLM personalization (LaMP) benchmark reveal statistically significant improvements in six out of seven datasets.