Summary
A research paper by Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, and Assaf Toledo (arXiv:2605.12487, May 2026) explores an LLM-guided query refinement paradigm that extends the usability of text embedding models to challenging zero-shot search and classification tasks. The method refines the embedding representation of a user query using feedback from a generative LLM on a small set of documents, allowing embeddings to adapt in real time to the target task without task-specific training. Experiments with state-of-the-art text embedding models across diverse search and classification benchmarks show 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. 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.
Paper Overview
- Field: NLP
- Authors: Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, Assaf Toledo
- Published: 2026-05-12
- arXiv: 2605.12487
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 improve ranking quality and induce clearer binary separation over the corpus, allowing the embedding space to better reflect the nuanced, task-specific constraints of each ad-hoc user query.
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