Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Soyeong Jeong1 Jinheon Baek2 Sukmin Cho1 Sung Ju Hwang1,2 Jong C. Park1 School of Computing1 Graduate School of AI2 Korea Advanced Institute of Science and Technology1,2 Corresponding author
Abstract
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA). However, even though there are various approaches dealing with queries of different complexities, they either handle simple queries with unnecessary computational overhead or fail to adequately address complex multi-step queries; yet, not all user requests fall into only one of the simple or complex categories. In this work, we propose a novel adaptive QA framework that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs from the simplest to the most sophisticated ones based on the query complexity. Also, this selection process is operationalized with a classifier, which is a smaller LM trained to predict the complexity level of incoming queries with automatically collected labels, obtained from actual predicted outcomes of models and inherent inductive biases in datasets. This approach offers a balanced strategy, seamlessly adapting between the iterative and single-step retrieval-augmented LLMs, as w
中文速览
大模型回答问题时常常出错，引入外部检索（RAG）能补充知识，但现有方案要么对所有问题都套用繁琐的多步检索、浪费算力，要么只做一次检索、搞不定需要多跳推理的复杂问题。Adaptive-RAG 提出用一个轻量分类器预判每个问题的复杂程度，再据此从"不检索""单步检索""多步迭代检索"三种策略中自动选最合适的一种来作答；这个分类器不需要人工打标签，而是利用各模型在真实数据上的预测结果以及数据集本身的归纳偏置自动生成训练标签。在多个开放域问答基准上的实验表明，该框架在准确率和计算效率上均优于现有自适应检索基线，简单问题不再"大炮打蚊子"，复杂问题也不会因策略太简单而答错，真正实现了按需分配资源。
原文 arXiv:2403.14403;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2403.14403v2