Task-Adaptive Embedding Refinement via Test-time LLM Guidance

Problem
This paper addresses the gap in the adaptability of embedding models for zero-shot search and classification tasks, particularly in scenarios where traditional methods may falter due to the lack of task-specific training data. The authors propose a novel approach that leverages large language models (LLMs) for query refinement at test time, enhancing the performance of embedding models without requiring extensive retraining. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the introduction of an LLM-guided query refinement mechanism. The authors utilize a generative LLM to provide feedback on a small set of documents related to the user query, which allows for real-time adaptation of the embedding representation. The architecture involves integrating the LLM’s output to refine the embeddings generated by state-of-the-art text embedding models. The training compute specifics are not disclosed, but the method is designed to operate efficiently in scenarios where full LLM pipelines are impractical. The approach is evaluated across various benchmarks, including literature search, intent detection, key-point matching, and nuanced query-instruction following.

Results
Empirical evaluations demonstrate that the LLM-guided query refinement leads to significant performance improvements. The authors report relative gains of up to +25% in various tasks compared to baseline models. For instance, in literature search tasks, the refined queries resulted in enhanced ranking quality and improved binary separation across the corpus. These results indicate that the embedding space becomes more aligned with the specific constraints of user queries, thereby enhancing the overall effectiveness of the embedding models in zero-shot scenarios.

Limitations
The authors acknowledge several limitations, including the dependency on the quality of the LLM’s feedback, which may vary based on the task and the documents provided. Additionally, the approach may not generalize well to all types of queries or domains, particularly those that are highly specialized or require domain-specific knowledge not captured by the LLM. The paper does not address the computational overhead introduced by the LLM guidance during inference, which could be a concern in resource-constrained environments.

Why it matters
This work has significant implications for the deployment of embedding models in real-world applications, particularly in contexts where labeled data is scarce or expensive to obtain. By enabling embedding models to adapt dynamically to user queries, the proposed method expands their usability in zero-shot settings, making them a viable alternative to more resource-intensive LLM pipelines. This advancement could facilitate broader adoption of embedding models in various domains, including information retrieval, conversational agents, and personalized search systems.

Authors: Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, Assaf Toledo
Source: arXiv:2605.12487
URL: https://arxiv.org/abs/2605.12487v1