Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
Faculty of Information Technology, Brno University of Technology（布尔诺理工大学信息技术学院）；Kempelen Institute of Intelligent Technologies（坎佩伦智能技术研究院）；University of Pittsburgh（匹兹堡大学）
Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to assess how different prompts, seed data selection strategies, filtering methods, or model settings affect the quality of paraphrased data (and downstream models). In this study, we investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. Using these incentive methods as part of instructions to LLMs augmenting text datasets, we measure their effects on generated texts lexical diversity and downstream model performance. We compare the effects over 5 different LLMs, 6 datasets and 2 downstream models. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；Institute of Science Tokyo（东京科学大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；UC Santa Barbara（加利福尼亚大学圣巴巴拉分校）；William & Mary（威廉玛丽学院）
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?
Faculty of Information Technology, Brno University of Technology（布尔诺理工大学信息技术学院）；Kempelen Institute of Intelligent Technologies（肯佩伦智能技术研究院）；University of Pittsburgh（匹兹堡大学）
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation
Utrecht University（乌得勒支大学）；Arab Academy for Science, Technology, and Maritime Transport（阿拉伯科技与海运学院）；Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Strong AI Lab, NAOInstitute, Waipapa Taumata Rau - The University of Auckland（强人工智能实验室、NAO研究所、怀帕帕·陶马陶·劳 - 奥克兰大学）；Xtracta, New Zealand（新西兰Xtracta公司）；University of Cambridge, UK（英国剑桥大学）；Sun Yat-Sen University, China（中国中山大学）；The University of Auckland, New Zealand（新西兰奥克兰大学）
CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples
DATUMO；AITRICS；Brainventures；Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University（Chung-Ang大学高级影像科学、多媒体与电影研究生院）；Department of Artificial Intelligence, Chung-Ang University（Chung-Ang大学人工智能系）
