Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, resulting in the generated documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation. Our code and generated documents can be found at https://github.com/wyu97/GenRead.
IRLab, Informatics Institute, UvA, Amsterdam, the Netherlands（IR实验室、信息研究所、乌得勒支大学、阿姆斯特丹、荷兰）；AI Lab, Pegasystems, Amsterdam, the Netherlands（AI实验室、佩加系统的阿姆斯特丹分部）；LIACS, Leiden University, Leiden, the Netherlands（LIACS、莱顿大学、莱顿、荷兰）
Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection
organization= School of Computer Science；Technology, Dalian University of Technology , addressline= No.2 Linggong Road, Ganjingzi District , city= Dalian , postcode= 116024 , country= China；organization= College of Health-Preservation；Wellness, Dalian Medical University , addressline= No. 9 West Section of Lvshun South Road, Lvshunkou District , city= Dalian , postcode= 116044 , country= China；organization= School of Information Engineering, Dalian Ocean University , addressline= No. 2-52, Heishijiao Street, Shahekou District , city= Dalian , postcode= 116023 , country= China；organization= School of Information Engineering, Liaodong University , addressline= No.116 Linjiang Back Street, Zhenan District , city= Dandong , postcode= 118001 , country= China；organization= Information Technology Center, Qinghai University , addressline= 251 Ningda Road, Chengbei District , city= Xining , postcode= 810016 , country= China；organization= School of Electronic；Information Engineering, Liaoning Technical University , addressline= 188 Longwan South Street, Sijiatun District , city= Huludao , postcode= 125105 , country= China；organization= Tencent (Dalian Northern Interactive Entertainment Technology Co., Ltd.) , addressline= 21/F, Tencent Building, No. 26 Jingxian St, Ganjingzi District , city= Dalian , postcode= 116085 , country= China
Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Vermont Artificial Intelligence Lab, Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系佛蒙特人工智能实验室）；Intelligent Machines Lab, Department of Artificial Intelligence, Information Technology University（信息技术大学人工智能系智能机器实验室）；Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电子与计算机工程系）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；AIIS, ASRI, INMC, and ISRC, Seoul National University（首尔国立大学人工智能研究所、人工智能研究室、智能网络研究中心和智能系统研究中心）；DGIST（延世大学）
Logic-Oriented Retriever Enhancement via Contrastive Learning
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China（上海人工智能教育研究院，华东师范大学）；Education Technology, East China Normal University, China（教育技术，华东师范大学）
Quantifying Retriever-Generator Alignment in RAG with Local Explanations
Department of Computer and Systems Sciences, Stockholm University（斯德哥尔摩大学计算机与系统科学系）；BIFOLD, Technische Universität Berlin（柏林技术大学BIFOLD）；Department of Informatics, Athens University of Economics and Business（雅典经济与商业大学信息系）；Archimedes, Athena Research Centre（雅典研究中心Archimedes）
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
School of Computer Science and Engineering, UESTC, Chengdu, China（电子科技大学计算机科学与工程学院，成都，中国）；IDEAS Lab, University of Birmingham, Birmingham, UK（伯明翰大学IDEAS实验室，英国布里斯托尔）
When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception
National University of Singapore（新加坡国立大学）；Wuhan University（武汉大学）；ByteDance（字节跳动）；Nankai University（南开大学）；JD Research, JD.com, Inc.（京东研究院（京东有限公司））；Zhejiang University（浙江大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The University of Hong Kong（香港大学）
