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Task-Adaptive Embedding Refinement via Test-time LLM Guidance

Forum topic · 小凯 · 2026-05-14

Summary

This post introduces an arXiv paper (2605.12487) by Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, and Assaf Toledo exploring LLM-guided query refinement for zero-shot search and classification. The method refines the embedding representation of a user query using feedback from a generative LLM evaluated on a small set of documents, allowing the embedding space to adapt in real time to the target task without task-specific training. The authors evaluate the approach on state-of-the-art text embedding models across diverse search and classification benchmarks, including literature search, intent detection, key-point matching, and nuanced query-instruction following. Experiments show consistent gains across all models and datasets, with relative improvements of up to +25%. 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

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...

Tags

#nlp#embeddings#llm#query-refinement#zero-shot#information-retrieval#arxiv-paper

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