Aligning Dense Retrievers with LLM Utility via Distillation
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 inje...
| Authors | Rajinder Sandhu, Di Mu, Cheng Chang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 April 2026 |
| Last updated at source | 29 May 2026 |
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