The quality of output from large language models (LLMs), particularly in machine translation (MT), is closely tied to the quality of in-context examples (ICEs) provided along with the query, i.e., the text to translate. The effectiveness of these ICEs is influenced by various factors, such as the domain of the source text, the order in which the ICEs are presented, the number of these examples, and the prompt templates used. Naturally, selecting the most impactful ICEs depends on understanding how these affect the resulting translation quality, which ultimately relies on translation references or human judgment. This paper presents a novel methodology for in-context learning (ICL) that relies on a search algorithm guided by domain-specific quality estimation (QE). Leveraging the XGLM model, our methodology estimates the resulting translation quality without the need for translation references, selecting effective ICEs for MT to maximize translation quality. Our results demonstrate significant improvements over existing ICL methods and higher translation performance compared to fine-tuning a pre-trained language model (PLM), specifically mBART-50.
NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs
Allen Institute for AI（艾伦人工智能研究所）；Association of American Medical Colleges, Center for Health Justice（美国医学院协会健康正义中心）；University of Washington（华盛顿大学）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）
SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Okinawa Institute of Science and Technology（冲绳科学技術研究院）；Nara Institute of Science and Technology（奈良科学技術研究院）
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）
Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（智能信息处理重点实验室，计算技术研究所，中国科学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Jarvis Research Center（贾维斯研究中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Separating Syntax from Language: A Mechanistic Account of Translation in Multilingual LLMs
Saarland University（萨尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for European Research in Trusted AI (CERTAIN)（欧洲可信人工智能研究中心）；University of Göttingen（哥廷根大学）
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Computer Science, University of Illinois at Chicago（芝加哥大学计算机科学系）
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains
United International University（国际联合大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Amazon GenAI（亚马逊生成人工智能）；University of Virginia（弗吉尼亚大学）；BRAC University（布拉克大学）
Vuyko Mistral: Adapting LLMs for Low-Resource Dialectal Translation
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"（乌克兰国家技术大学 "伊戈尔·西科斯基基土夫技术学院"）；Ukrainian Catholic University（乌克兰天主教大学）
