This repository maintains an organized list of academic papers examining the intersection of LLMs and recommendation systems. It includes survey papers reviewing the field and individual works covering various approaches such as prompt-tuning, instruction-following recommendation, privacy-preserving synthetic query generation, and interactive explanation generation. The collection documents how foundation models are being adapted and applied to improve recommendation quality, interpretability, and user interaction.