Open-Source Large Language Models as Multilingual Crowdworkers: Synthesizing Open-Domain Dialogues in Several Languages With No Examples in Targets and No Machine Translation
The prevailing paradigm in the domain of Open-Domain Dialogue agents predominantly focuses on the English language, encompassing both models and datasets. Furthermore, the financial and temporal investments required for crowdsourcing such datasets for finetuning are substantial, particularly when multiple languages are involved. Fortunately, advancements in Large Language Models (LLMs) have unveiled a plethora of possibilities across diverse tasks. Specifically, instruction-tuning has enabled LLMs to execute tasks based on natural language instructions, occasionally surpassing the performance of human crowdworkers. Additionally, these models possess the capability to function in various languages within a single thread. Consequently, to generate new samples in different languages, we propose leveraging these capabilities to replicate the data collection process. We introduce a pipeline for generating Open-Domain Dialogue data in multiple Target Languages using LLMs, with demonstrations provided in a unique Source Language. By eschewing explicit Machine Translation in this approach, we enhance the adherence to language-specific nuances. We apply this methodology to the PersonaChat dataset. To enhance the openness of generated dialogues and mimic real life scenarii, we added the notion of speech events corresponding to the type of conversation the speakers are involved in and also that of common ground which represents the premises of a conversation.
MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning
BRAC University（布拉克大学）；United International University（国际联合大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Center for Computational & Data Sciences, Independent University, Bangladesh（计算与数据科学中心，独立大学，孟加拉国）；Amazon GenAI（亚马逊生成人工智能）；University of Virginia（弗吉尼亚大学）
KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；USTC Knowledge Computing Lab（中国科学技术大学知识计算实验室）
LLMs are Also Effective Embedding Models: An In-depth Overview
Beihang University（北航大学）；SKLSDE Lab, Beihang University（北航SKLSDE实验室）；University of Technology Sydney（悉尼大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）
