零样本搜索和分类是信息检索的硬骨头，IBM用LLM引导查询精调把嵌入模型性能拉高25%，做搜索/分类的团队可以直接拿开源代码试试，成本比全量LLM推理低得多。
IBM研究团队提出一种LLM引导的查询精调范式，通过生成式LLM对少量文档的反馈，实时优化用户查询的嵌入表示，从而扩展嵌入模型在零样本搜索和分类任务中的适用性。实验表明，该方法在文献搜索、意图检测、关键点匹配等任务上带来最高25%的相对提升，且能改善排序质量和类别分离度。该方案让嵌入模型在无法大规模使用LLM的场景下成为有竞争力的替代方案，代码已开源。
Task-Adaptive Embedding Refinement via Test-time LLM Guidance
We explore the effectiveness of an LLM-guided query refinement paradigm for extending the usability of embedding models to challenging zero-shot search and classification tasks. Our approach refines the embedding representation of a user query using feedback from a generative LLM on a small set of documents, enabling embeddings to adapt in real time to the target task. We conduct extensive experiments with state-of-the-art text embedding models across a diverse set of challenging search and classification benchmarks. Empirical results indicate that LLM-guided query refinement yields consistent gains across all models and datasets, with relative improvements of up to +25% in literature search, intent detection, key-point matching, and nuanced query-instruction following. The refined queries improve ranking quality and induce clearer binary separation across the corpus, enabling the embedding space to better reflect the nuanced, task-specific constraints of each ad-hoc user query. Importantly, this expands the range of practical settings in which embedding models can be effectively deployed, making them a compelling alternative when costly LLM pipelines are not viable at corpus-scale. We release our experimental code for reproducibility, at https://github.com/IBM/task-aware-embedding-refinement.