Aligning Dense Retrievers with LLM Utility via Distillation

Rajinder Sandhu; Di Mu; Cheng Chang; Md Shahriar Tasjid; Himanshu Rai; Maksims Volkovs; Ga Wu

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arXiv · 2604.22722

Aligning Dense Retrievers with LLM Utility via Distillation

Rajinder Sandhu·Di Mu·Cheng Chang·Md Shahriar Tasjid·Himanshu Rai·Maksims Volkovs·Ga Wu

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Abstract

Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations. Conversely, utility-based approaches leveraging LLM re-ranking often achieve superior performance but are computationally prohibitive and prone to noise inherent in perplexity estimation. We propose Utility-Aligned Embeddings (UAE), a framework designed to merge these advantages into a practical, high-performance retrieval method. We formulate retrieval as a distribution matching problem, training a bi-encoder to imitate a utility distribution derived from perplexity reduction using a Utility-Modulated InfoNCE objective. This approach injects graded utility signals directly into the embedding space without requiring test-time LLM inference. On the QASPER benchmark, UAE improves retrieval Recall@1 by 30.59%, MAP by 30.16% and Token F1 by 17.3% over the strong semantic baseline BGE-Base. Crucially, UAE is over 180x faster than the efficient LLM re-ranking methods preserving competitive performance, demonstrating that aligning retrieval with generative utility yields reliable contexts at scale.

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Rajinder Sandhu, Di Mu, Cheng Chang, Md Shahriar Tasjid, Himanshu Rai, Maksims Volkovs, Ga Wu. 2026-04-24. Aligning Dense Retrievers with LLM Utility via Distillation. https://arxiv.org/abs/2604.22722

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