Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation

arXiv:2404.05970v1 Announce Type: new
Abstract: This paper studies retrieval-augmented approaches for personalizing large language models (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 large language models 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 …

abstract applications arxiv augmentation cs.cl cs.ir documents domains impact language language models large language large language models llms optimization paper retrieval retrieval-augmented studies through type