关键词: 大语言模型, 零样本学习, 重排序, 信息检索, 提示工程

Abstract: To address the challenges of insufficient label semantics understanding, vague relationship modeling, and high computational costs in zero-shot re-ranking tasks for large language models (LLMs), a hierarchical filtering and label semantics extension approach named HFLS was proposed. A multi-level label semantic extension path was constructed, and a progressive prompting strategy ("keyword matching → semantic association → domain knowledge integration") was designed to guide LLM in deep relevance reasoning. A hierarchical filtering mechanism was introduced to reduce computational complexity while retaining high-potential candidate documents. Experimental results indicate that on seven benchmark datasets (e.g., TREC DL 2019), HFLS achieves average gains of 21.92%, 13.43% and 8.59%in NDCG@10 compared to Pointwise methods like Pointwise.qg, Pointwise.yes_no, and Pointwise.3Label. In terms of inference efficiency, the processing latency per query is reduced by 91.06% compared to Listwise methods, 68.87% compared to Pairwise methods, and 33.54% compared to Setwise methods.

Key words: large language model, zero-shot learning, re-ranking, information retrieval, prompt engineering.