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

• URL: http://arxiv.org/abs/2603.13776v1
• Date: Sat, 14 Mar 2026 05:59:12 GMT
• Title: Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion
• Abstract summary: A retrieval-feedback-driven framework is proposed to transfer retrieval-friendly expansion behavior from a strong teacher model to a compact student model.<n>A retrieval-metric-driven strategy is then introduced to automatically form chosen/rejected expansion pairs according to nDCG@10 differences.<n>Experiments on TREC DL19/20/21 and MIRACL-zh show that the proposed approach preserves strong retrieval effectiveness while substantially reducing inference cost.
• Score: 20.96368967546077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Rather than relying on few-shot exemplars at inference time, the framework first leverages two complementary types of teacher-generated expansions, produced under zero-shot and few-shot prompting conditions, as supervision signals for distillation and as candidate pools for preference construction. A retrieval-metric-driven strategy is then introduced to automatically form chosen/rejected expansion pairs according to nDCG@10 differences, and Direct Preference Optimization is applied to explicitly align generation preferences with retrieval objectives. Experiments on TREC DL19/20/21 and MIRACL-zh show that the proposed approach preserves strong retrieval effectiveness while substantially reducing inference cost. In particular, the distilled Qwen3-4B model reaches about 97% of the teacher (DeepSeek-685B) model's nDCG@10 performance on DL19, and remains effective on the Chinese MIRACL-zh benchmark, demonstrating strong practicality across both English and Chinese retrieval settings.

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