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 well as the no-retrieval methods, in response to a range of query complexities. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems, compared to relevant baselines including the adaptive retrieval approaches. Code is available at: https://github.com/starsuzi/Adaptive-RAG.
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations
Idiap Research Institute（Idiap研究机构）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；National Biomarker Centre (NBC), CRUK Manchester Institute（国家生物标志物中心（NBC），CRUK曼彻斯特研究所）
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing National Research Center for Information Science and Technology - Tsinghua University（北京信息科学与技术国家研究中心-清华大学）
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Wuhan University of Science and Technology（武汉科技大学）；National University of Singapore（新加坡国立大学）；Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；BOSS Zhipin（BOSS直聘）
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue
Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Science and Technology of China（中国科学技术大学）；Beihang University（北京航空航天大学）
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Department of Mathematical Sciences, Tsinghua University（清华大学数学科学系）
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Delft University of Technology（代尔夫特理工大学）；Netflix Eyeline Studios；University of California, Berkeley（加州大学伯克利分校）；University of Chicago（芝加哥大学）
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Cleveland State University（克利夫兰州立大学）；Kent State University（肯特州立大学）；Northeastern University（东北大学）；Roux Institute at Northeastern University（东北大学鲁克斯研究所）；University of Agder (UiA)（阿格德尔大学）
RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；City University of Hong Kong（香港城市大学）；Virginia Tech（弗吉尼亚理工大学）；University of South Florida（南佛罗里达大学）；Rochester Institute of Technology（罗切斯特理工学院）
