Instance-aware Prompt Learning for Language Understanding and Generation
National Laboratory of Pattern Recognition, Institute of Automation, CAS（中国科学院自动化研究所模式识别国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of parameters. The current usage of discrete and continuous prompts assumes that the prompt is fixed for a specific task and all samples in the task share the same prompt. However, a task may contain quite diverse samples in which some are easy and others are difficult, and diverse prompts are desirable. In this paper, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method obtains considerable improvements compared to strong baselines. Especially, our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Alibaba Cloud Computing（阿里巴巴云计算）；Shanghai Jiao Tong University（上海交通大学）；College of Software, Zhejiang University（浙江大学软件学院）
HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
HausaNLP（豪萨NLP）；Kaduna State University（卡杜纳州立大学）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；University of the Witwatersrand（金山大学）；Northeastern University（东北大学）；Imperial College（帝国理工学院）；Data Science for Social Impact, University of Pretoria（比勒陀利亚大学社会影响数据科学中心）
DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University（上海教育委员会智能交互与认知工程重点实验室）；Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3（上海Web3可信数据流通与治理重点实验室）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Xiaomi, Beijing, China（小米（北京））
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明的负责任人工智能与数据分析（PRADA）实验室）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；South China University of Technology（华南理工大学）；University of Macau（澳门大学）；Nankai University（南开大学）；University of Copenhagen（哥本哈根大学）；Stony Brook University（纽约州立大学石溪分校）；SDAIA-KAUST AI（沙特数据与人工智能总局-阿卜杜拉国王科技大学人工智能机构）；Ant Group（蚂蚁集团）
PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning
School of Cyberspace Security, Beijing University of Posts and Telecommunications（网络安全学院，北京邮电大学）；Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education（可信分布式计算与服务重点实验室（北京邮电大学））；QI-ANXIN Group（QI-ANXIN集团）；RapidAI Research（RapidAI研究院）；ZGC Lab（ZGC实验室）
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion
Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）；Research Institute of Multiple Agents and Embodied Intelligence, Pengcheng Laboratory（多智能体与具身智能研究院，鹏城实验室）
