Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models
Tsinghua University（清华大学）；Princeton University（普林斯顿大学）；Renmin University of China（中国人民大学）；Tencent Inc（腾讯公司）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；International Innovation Center of Tsinghua University（清华大学国际创新中心）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Prompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models. However, one pitfall of prompting is the need of manually-designed patterns, whose outcome can be unintuitive and requires large validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully automatic prompting method: (1) We adopt natural language prompts on sequence-to-sequence models, enabling free-form generation and larger label search space; (2) We propose label sequences -- phrases with indefinite lengths to verbalize the labels -- which eliminate the need of manual templates and are more expressive than single label words; (3) We use beam search to automatically generate a large amount of label sequence candidates and propose contrastive re-ranking to get the best combinations. AutoSeq significantly outperforms other no-manual-design methods, such as soft prompt tuning, adapter tuning, and automatic search on single label words; the generated label sequences are even better than curated manual ones on a variety of tasks. Our method reveals the potential of sequence-to-sequence models in few-shot learning and sheds light on a path to generic and automatic prompting. The source code of this paper can be obtained from https://github.com/thunlp/Seq2Seq-Prompt.
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Center for Information and Language Processing (CIS)（信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；ScaDS.AI and TU Dresden（ScaDS.AI 和 梅克伦堡-前波美拉尼亚技术大学）；Department of Statistics, LMU Munich（统计学系，慕尼黑大学）；University of Maryland, College Park（马里兰大学 College Park 分校）
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
MGH Institute for Technology Assessment, Harvard Medical School（哈佛医学院MGH技术评估研究所）；Melbourne Business School, University of Melbourne（墨尔本大学墨尔本商学院）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning
Hainan University（海南大学）；Nanyang Technological University（南洋理工大学）；Hithink Research（慧思研究）；HKUST (Guangzhou)（香港科技大学（广州））；IDEA Research（IDEA研究院）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；Nanjing University of Science and Technology（南京理工大学）
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators
The Conversational Artificial Intelligence (CoAI) Group, Tsinghua University（清华大学对话人工智能（CoAI）小组）；University of Electronic Science and Technology of China（电子科技大学）；Zhipu AI（智谱AI）；The Knowledge Engineering Group (KEG), Tsinghua University（清华大学知识工程小组（KEG））
Simple LLM Prompting is State-of-the-Art for Robust and Multilingual Dialogue Evaluation
INESC-ID；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Faculdade de Letras, University of Lisbon（里斯本大学文学院）；Carnegie Mellon University（卡内基梅隆大学）；Phrase（Phrase公司）
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Enuma Inc（Enuma公司）；University of Memphis（孟菲斯大学）；Daegu National University of Education（大邱教育大学）；Seoul National University（首尔大学）；Seoul Metropolitan Office of Education（首尔市教育厅）；Korea University（高丽大学）
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Zhejiang University（浙江大学）；AZFT Joint Lab for Knowledge Engine（AZFT知识引擎联合实验室）；Hangzhou Innovation Center, Zhejiang University（浙江大学杭州国际科创中心）；Alibaba Group（阿里巴巴集团）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）
Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents
Bonn-Aachen International Center for Information Technology, University of Bonn, Germany（波恩-阿赫恩国际信息科技中心，波恩大学，德国）；Lamarr Institute for Machine Learning and Artificial Intelligence, Germany（拉马尔机器学习与人工智能研究所，德国）
ART: Automatic multi-step reasoning and tool-use for large language models
University of Washington（华盛顿大学）；Microsoft Research（微软研究院）；University of California, Irvine（加州大学欧文分校）；Allen Institute of Artificial Intelligence（艾伦人工智能研究所）；Meta AI
