Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion
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 compl
Record details
Published: 14 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Children & education
Retrieved: 14 July 2026
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ethics.ai (14 March 2026), “Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion,” evidence record 7253, https://ethics.ai/record/7253 (originally published by arXiv).
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