Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both language understanding and generation tasks. In this work, we study the problem of prompt tuning for neural text retrievers. We introduce parameter-efficient prompt tuning for text retrieval across in-domain, cross-domain, and cross-topic settings. Through an extensive analysis, we show that the strategy can mitigate the two issues -- parameter-inefficiency and weak generalizability -- faced by fine-tuning based retrieval methods. Notably, it can significantly improve the out-of-domain zero-shot generalization of the retrieval models. By updating only 0.1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated. Finally, to facilitate research on retrievers' cross-topic generalizability, we curate and release an academic retrieval dataset with 18K query-results pairs in 87 topics, making it the largest topic-specific one to date.
Omni-Modal Dissonance Benchmark: Systematically Breaking Modality Consensus to Probe Robustness and Calibrated Abstention
University of California, Riverside（加州大学河滨分校）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）
K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
University of Roehampton（罗汉普顿大学）；Kivira Health；University of Hertfordshire（赫特福德大学）；University of Surrey（萨里大学）；University of Bedfordshire（贝德福德大学）；Tavistock Relationships（塔维斯托克关系中心）；InsideOut
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever
University of Science and Technology of China（中国科学技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
Waseda University（早稻田大学）；Northwestern University（西北大学）；RIKEN AIP（理化学研究所革新智能研究中心）；Snowflake Inc.（Snowflake公司）；University of Utah（犹他大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；The University of Tokyo（东京大学）
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校（UIUC））；Imperial College London（伦敦帝国学院）
R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
School of Computer Science, South China Normal University（华南师范大学计算机学院）；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）
S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）
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）
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）
