Task-Adaptive Embedding Refinement via Test-time LLM Guidance
For practitioners using embedding models in zero-shot settings, this method offers a lightweight way to adapt queries to task-specific constraints without full LLM pipelines.
The paper proposes a method that uses a generative LLM to refine query embeddings on a small document set, improving zero-shot search and classification. Experiments show consistent gains across models and datasets, with up to +25% relative improvement in tasks like literature search and intent detection.
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.