Existing information retrieval (IR) models often assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings where retrieval is inherently heterogeneous and diverse. In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous knowledge retriever that (1) builds a unified retrieval space for heterogeneous knowledge and (2) follows diverse user instructions to retrieve knowledge of specified types. UniHGKR consists of three principal stages: heterogeneous self-supervised pretraining, text-anchored embedding alignment, and instruction-aware retriever fine-tuning, enabling it to generalize across varied retrieval contexts. This framework is highly scalable, with a BERT-based version and a UniHGKR-7B version trained on large language models. Also, we introduce CompMix-IR, the first native heterogeneous knowledge retrieval benchmark. It includes two retrieval scenarios with various instructions, over 9,400 question-answer (QA) pairs, and a corpus of 10 million entries, covering four different types of data. Extensive experiments show that UniHGKR consistently outperforms state-of-the-art methods on CompMix-IR, achieving up to 6.36% and 54.23% relative improvements in two scenarios, respectively. Finally, by equipping our retriever for open-domain heterogeneous QA systems, we achieve a new state-of-the-art result on the popular ConvMix task, with an absolute improvement of up to 5.90 points.
Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model
Department of Gastroenterology, Renmin Hospital, Wuhan University（武汉大学人民医院消化内科）；School of Computer Science, Wuhan University（武汉大学计算机学院）；State Grid Corporation（国家电网公司）
FollowTable: A Benchmark for Instruction-Following Table Retrieval
Southeast University（东南大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（新一代人工智能技术及其交叉应用国家重点实验室（东南大学））；Its Interdisciplinary Applications (Southeast University), Ministry of Education（交叉应用（东南大学），教育部）
A Comparative Study of Specialized LLMs as Dense Retrievers
Key Laboratory of Network Data Science and Technology（网络数据科学与技术重点实验室）；Institute of Computing Technology（计算技术研究所）；Chinese Academy of Sciences（中国科学院）；State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；University of Chinese Academy of Science（中国科学院大学）
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Université de Montréal（蒙特利尔大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Waseda University（早稻田大学）
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
University of Waterloo（滑铁卢大学）；Georgia Institute of Technology（佐治亚理工学院）；Hong Kong University of Science and Technology（香港科技大学）；Google DeepMind（谷歌DeepMind）
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of AI Safety, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室）
IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Zhejiang University（浙江大学）；Yale University（耶鲁大学）
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
BOSS Zhipin（BOSS直聘）；University of Science and Technology of China（中国科学技术大学）；PBC School of Finance, Tsinghua University（清华大学五道口金融学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory（中关村实验室）；The Hong Kong University of Science and Technology (GuangZhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州港科大霍英东研究院）
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags
Ahsanullah University of Science & Technology（阿萨努拉科技大学）；University of Alberta（阿尔伯塔大学）；Université de Sherbrooke（谢布鲁克大学）；Salesforce Research（Salesforce研究院）；Nanyang Technological University（南洋理工大学）
Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning
School of Mechanical, Aerospace, and Manufacturing Engineering University of Connecticut Storrs, Connecticut, USA（机械、航空航天与制造工程学院 美国康涅狄格大学 斯托尔斯分校）；The United States Army Combat Capabilities Development Command Ground Vehicle Systems Center Warren, Michigan, USA（美国陆军作战能力发展司令部地面车辆系统中心 华伦，密歇根州，美国）
