most citedRQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

17 citations · 17 across the 3 of their papers we have counts for

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cs.CL2024

Training Task Experts through Retrieval Based Distillation

Jiaxin Ge, Xueying Jia, Vijay Viswanathan +2

One of the most reliable ways to create deployable models for specialized tasks is to obtain an adequate amount of high-quality task-specific data. However, for specialized tasks,…

cs.CL2024★ 17 cited

RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

Chi-Min Chan, Chunpu Xu, Ruibin Yuan +4

Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pr…