arxiv preprint - Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

In this episode, we discuss Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity by Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park. The paper introduces an adaptive QA model that optimizes the balance between efficiency and accuracy by choosing the appropriate response strategy for questions of varying complexity. A smaller language model classifies the question's complexity level, enabling the system to switch between different retrieval-augmented LLM strategies and even non-retrieval methods. The model outperforms existing baselines on various open-domain QA datasets, and the authors have made the code available on GitHub.