APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Queen Mary University of London（伦敦大学玛丽女王学院）；University of Helsinki（赫尔辛基大学）；The Hong Kong Polytechnic University（香港理工大学）
Recent research has highlighted the potential of LLM applications, like ChatGPT, for performing label annotation on social computing text. However, it is already well known that performance hinges on the quality of the input prompts. To address this, there has been a flurry of research into prompt tuning -- techniques and guidelines that attempt to improve the quality of prompts. Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. APT-Pipe aims to automatically tune prompts to enhance ChatGPT's text classification performance on any given dataset. We implement APT-Pipe and test it across twelve distinct text classification datasets. We find that prompts tuned by APT-Pipe help ChatGPT achieve higher weighted F1-score on nine out of twelve experimented datasets, with an improvement of 7.01% on average. We further highlight APT-Pipe's flexibility as a framework by showing how it can be extended to support additional tuning mechanisms.
All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction
Nankai University（南开大学）；Tokyo Institute of Technology（东京工业大学）；Microsoft Research Asia（微软亚洲研究院）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院（BAAI））
Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
NOVA Information Management School (NOVA IMS)（NOVA信息管理学校）；Universidade Nova de Lisboa（里斯本新大学）；Centro Algoritmi/LASI（算法中心/LASI）；University of Évora（埃维拉大学）
