CDS4RAG：基于循环双序列的RAG超参数优化
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
AI总结：
本文提出CDS4RAG框架，通过循环双序列方法优化RAG的全部超参数，提升检索生成性能，实验显示在24种情况下均优于现有算法。
摘要：
检索增强生成（RAG）对检索器和生成器的大量超参数敏感，但使用给定查询优化这些超参数具有挑战性，因为存在复杂的交互和昂贵的评估成本。现有算法效果差且收敛慢，因为它们通常将RAG视为单一黑箱或仅优化部分超参数。本文提出CDS4RAG，一种通过新循环双序列方法利用给定查询优化全部RAG超参数的框架。CDS4RAG的特殊之处在于区分检索器和生成器的超参数，并交替优化。这种范式允许设计细粒度的循环内预算分配，并通过跨循环种子加速优化生成器。CDS4RAG也是算法无关的框架，可以与多种通用算法配对。通过在四个常见基准和两个基础LLM上的实验，我们发现CDS4RAG在21/24种情况下显著提升普通算法性能，并在所有情况下显著优于最先进算法，生成质量提升达1.54倍，速度提升更优。
英文摘要：
Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries is a challenging task due to the complex interactions and expensive evaluation costs. Existing algorithms are ineffective and slow in convergence, since they often treat RAG as a monolithic black box or only optimize partial hyperparameters. In this paper, we propose CDS4RAG, a framework that optimizes the full RAG hyperparameters using given queries via a new cyclic dual-sequential formulation. CDS4RAG is special in the sense that it distinguishes the hyperparameters of the retriever and generator, cyclically optimizing them in turn. Such a paradigm allows us to design fine-grained within-cycle budget provision and expedite the optimization via cross-cycle seeding when optimizing the generator. CDS4RAG is also an algorithm-agnostic framework that can be paired with diverse general algorithms. Through experiments on four common benchmarks and two backbone LLMs, we reveal that CDS4RAG considerably boosts the vanilla algorithms in 21/24 cases while significantly outperforming state-of-the-art algorithms in all cases with up to 1.54x improvements of generation quality and better speedup.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
