ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Researchers have developed ProfiLLM, a novel agentic LLM data pipeline designed to enhance industrial ride-hailing dispatch systems. This system addresses challenges in processing massive datasets by using tool-augmented knowledge mining and utility-aligned profile exploration. When deployed on DiDi's platform, ProfiLLM demonstrated significant improvements, including a +6.14% relative AUC increase in outcome prediction and a +4.35% GMV gain in simulations. AI
IMPACT Proposes a novel method for applying LLMs to large-scale industrial data, potentially improving efficiency in logistics and dispatch systems.