Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion

ArXi:2603.13776v1 Announce Type: cross Large language models have recently enabled a generative paradigm for query expansion, but their high inference cost makes direct deployment difficult in practical retrieval systems. To address this issue, a retrieval-feedback-driven distillation and preference-alignment framework is proposed to transfer retrieval-friendly expansion behavior from a strong teacher model to a compact student model.