Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning
Qiannan Normal University for Nationalities（黔南民族师范学院）；Guangzhou University（广州大学）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）
Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models.
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
Institute of AI for Education, East China Normal University（华东师范大学教育人工智能研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Shanghai Changning Mental Health Center（上海市长宁区精神卫生中心）
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering（信息工程研究所）；Beijing Institute of Technology（北京理工大学）；Guangzhou University（广州大学）
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Institute for Clarity in Documentation（文档清晰性研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗克库尔中心）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学清华大学深圳国际研究生院）；School of Automation, Wuhan University of Technology（武汉理工大学自动化学院）
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Department of Biomedical Informatics, School of Medicine, Emory University（生物医学信息学系，医学院，埃默里大学）；Department of Computer Science, Emory University（计算机科学系，埃默里大学）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国立卫生研究院）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）
Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting
大语言模型已经表明，通过自然语言提示可以实现令人印象深刻的zero-shot性能（Radford et al.，2019; Brown等人，2020; Sanh等人，2021年）。然而，创建一个有效的提示需要大量的尝试和错误。这就向\textit{prompts}提出了一个问题：提示的质量如何影响它的表现？为此，我们从各种任务中收集并标准化提示，以用于它们不是为之设计的任务。然后，我们在固定的多项选择数据集上评估这些提示，以定量分析提示的某些属性如何影响性能。我们发现，包括选择和使用提示不使用在预培训提供了显着的改善。所有的实验和代码都可以在https://github.com/gabeorlanski/zero-shot-cross-task上找到。
Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国家台湾大学计算机科学与信息工程系）；Institute of Information Science, Academia Sinica, Taiwan（学术院信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国家台湾大学人工智能研究中心（AINTU））
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）
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Singapore Management University（新加坡管理大学）；Southwest Jiaotong University（西南交通大学）；Singapore University of Technology and Design（新加坡科技设计大学）；East China Normal University（华东师范大学）
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Key Laboratory of Machine Perceptron (MOE), Peking University（北京大学机器感知教育部重点实验室）
Hint of Pseudo Code (HoPC): Zero-Shot Step by Step Pseudo Code Reasoning Prompting
Department of Computer Science and Technology（计算机科学与技术系）；Institute for Artificial Intelligence at Tsinghua University（清华大学人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学国家研究中心）；Tsinghua University（清华大学）
