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Aligning Dense Retrievers with LLM Utility via Distillation

arXiv · Apr 24

Researchers proposed Utility-Aligned Embeddings (UAE), a new framework designed to enhance Retrieval-Augmented Generation (RAG) by merging the precision of LLM re-ranking with the efficiency of dense vector retrieval. UAE trains a bi-encoder to imitate an LLM utility distribution using a Utility-Modulated InfoNCE objective, injecting graded utility signals directly into the embedding space. On the QASPER benchmark, UAE improved retrieval Recall@1 by 30.59% and was over 180 times faster than efficient LLM re-ranking methods while preserving competitive performance. Why it matters: This approach offers a practical way to significantly improve the accuracy and speed of RAG systems by providing more reliable contexts at scale without heavy computational cost.

Aligning Dense Retrievers with LLM Utility via DistillationAligning Dense Retrievers with LLM Utility via Distillation

arXiv · Apr 24

Researchers proposed Utility-Aligned Embeddings (UAE), a new framework to improve dense vector retrieval for Retrieval-Augmented Generation (RAG) by aligning it with LLM utility. UAE trains a bi-encoder to imitate an LLM's utility distribution, derived from perplexity reduction, using a Utility-Modulated InfoNCE objective. On the QASPER benchmark, UAE achieved a 30.59% improvement in Recall@1 and was over 180 times faster than efficient LLM re-ranking methods while preserving competitive performance. Why it matters: This approach offers a significant leap in RAG efficiency and accuracy, providing a method to align retrieval with generative utility without test-time LLM inference, which could enable more scalable and precise LLM applications.