DaiLLM

We propose a large language model (LLM)-based agent framework that innovatively incorporates the Chain-of-Thought (CoT) approach and Reinforcement Learning from AI Feedback (RLAIF) mechanisms to simulate human mobility, namely Daily mobility LLM (DaiLLM). Our framework consists of three modules: the Profiler derives user characteristics from historical trajectory data; the Generator simulates movement through CoT process, which integrates constraints from Profiler outputs; and the Discriminator employs RLAIF to evaluate similarity between generated results and real data, providing feedback to optimize results.