Abstract
With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pioneering strategies have primarily transformed traditional recommendation tasks into challenges of natural language generation, there has been a relative scarcity of exploration in the domain of session-based recommendation