Embedding-based Retrieval Models (ERMs) have emerged as a promising framework for large-scale text retrieval problems due to powerful large language models. Nevertheless, fine-tuning ERMs to reach state-of-the-art results can be expensive due to the extreme scale of data as well as the complexity of multi-stages pipelines (e.g., pre-training, fine-tuning, distillation). In this work, we propose the PEFA framework, namely ParamEter-Free Adapters, for fast tuning of ERMs without any backward pass in the optimization. At index building stage, PEFA equips the ERM with a non-parametric k-nearest neighbor (kNN) component. At inference stage, PEFA performs a convex combination of two scoring functions, one from the ERM and the other from the kNN. Based on the neighborhood definition, PEFA framework induces two realizations, namely PEFA-XL (i.e., extra large) using double ANN indices and PEFA-XS (i.e., extra small) using a single ANN index. Empirically, PEFA achieves significant improvement on two retrieval applications. For document retrieval, regarding Recall@100 metric, PEFA improves not only pre-trained ERMs on Trivia-QA by an average of 13.2%, but also fine-tuned ERMs on NQ-320K by an average of 5.5%, respectively. For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5.3% and 14.5%, for PEFA-XS and PEFA-XL, respectively. Our code is available at https://github.com/amzn/pecos/tree/mainline/examples/pefa-wsdm24.
UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval
Zhejiang University（浙江大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Dalian University of Technology（大连理工大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Microsoft Research（微软研究院）
R$^{2}$Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；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）；Meituan（美团）
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval
School of Computer Science and Technology, Xi’an University of Posts and Telecommunications（西安邮电大学计算机科学与技术学院）；Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing（陕西省网络数据分析与智能处理重点实验室）；Xi’an Key Laboratory of Big Data and Intelligent Computing（西安大数据与智能计算重点实验室）；School of Computer Engineering, Weifang University（潍坊大学计算机工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Electronic and Electrical Engineering, Brunel University of London（伦敦布鲁内尔大学电子与电气工程系）；Dept. Matemàtiques i Informàtica and Institute of Neuroscience, Univeritat de Barcelona（数学与计算机系和神经科学研究所，巴塞罗那大学）
Improving Robustness of Tabular Retrieval via Representational Stability
Department of Computer Science Rensselaer Polytechnic Institute（罗切斯特理工学院计算机科学系）；Department of Computer Science Arizona State University（亚利桑那州立大学计算机科学系）；ScaleAI
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Department of Computer Science, University of California, Los Angeles, USA（加州大学洛杉矶分校计算机科学系）；Sun Yat-sen University Shenzhen Campus, School of Cyber Science and Technology, Shenzhen, China（中山大学深圳校区信息科学与技术学院）
Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization
Microsoft（微软公司）；Shanghai International Studies University（上海国际问题研究大学）；Zhejiang University（浙江大学）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（数据科学与人工智能系，香港理工大学）
Autoregressive Retriever: Improving Query Understanding from Item Feedback for Universal Multimodal Retrieval
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）；Zhongguancun Academy（中关村实验室）；Xiaohongshu（小红书）；Southeast Academy of Information Technology, Beijing Institute of Technology（北京理工大学东南信息科学研究院）；Zhongguancun Laboratory（中关村实验室）
