Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translation tasks. To address this issue, we propose an adaptive few-shot prompting (AFSP) framework to automatically select suitable translation demonstrations for various source input sentences to further elicit the translation capability of an LLM for better machine translation. First, we build a translation demonstration retrieval module based on LLM's embedding to retrieve top-k semantic-similar translation demonstrations from aligned parallel translation corpus. Rather than using other embedding models for semantic demonstration retrieval, we build a hybrid demonstration retrieval module based on the embedding layer of the deployed LLM to build better input representation for retrieving more semantic-related translation demonstrations. Then, to ensure better semantic consistency between source inputs and target outputs, we force the deployed LLM itself to generate multiple output candidates in the target language with the help of translation demonstrations and rerank these candidates. Besides, to better evaluate the effectiveness of our AFSP framework on the latest language and extend the research boundary of neural machine translation, we construct a high-quality diplomatic Chinese-English parallel dataset that consists of 5,528 parallel Chinese-English sentences. Finally, extensive experiments on the proposed diplomatic Chinese-English parallel dataset and the United Nations Parallel Corpus (Chinese-English part) show the effectiveness and superiority of our proposed AFSP.
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）
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））
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）
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
Shanghai Jiao Tong University（上海交通大学）；SJTU-Paris Elite Institute of Technology, Shanghai Jiao Tong University（上海交通大学巴黎卓越工程师学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；College of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国立台湾大学计算机科学与资讯工程学系）；Institute of Information Science, Academia Sinica, Taiwan（台湾“中央研究院”信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国立台湾大学人工智能研究中心）
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
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）；Kuaishou Tech, Co.（快手科技有限公司）；Institute for AI Industry Research, Tsinghua University（清华大学智能产业研究院）；International Innovation Center of Tsinghua University（清华大学上海国际创新中心）
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）
Revisiting Direct Speech-to-Text Translation with Speech LLMs: Better Scaling than CoT Prompting?
Barcelona Supercomputing Center, Spain（巴塞罗那超级计算中心，西班牙）；Universitat Politècnica de Catalunya, Spain（加泰罗尼亚理工大学，西班牙）；DFKI GmbH, Saarland Informatics Campus, Saarbrücken, Germany（德意志联邦计算机研究中心（DFKI） GmbH，萨尔布吕肯，德国）
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）
