Paper Overview
Field: NLP Authors: Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, Assaf Toledo Published: 2026-05-12 arXiv: 2605.12487
Summary
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.
Key Findings
- Extensive experiments were conducted with state-of-the-art text embedding models across a diverse set of challenging search and classification benchmarks.
- LLM-guided query refinement yields consistent gains across all models and datasets.
- Relative improvements of up to +25% are reported in literature search, intent detection, key-point matching, and nuanced query-instruction following.
- Refined queries improve ranking quality and induce clearer binary separation over the corpus, making the embedding space better reflect the nuanced, task-specific constraints of each ad hoc user query.
Original Abstract
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...