Few-shot learning has drawn researchers' attention to overcome the problem of data scarcity. Recently, large pre-trained language models have shown great performance in few-shot learning for various downstream tasks, such as question answering and machine translation. Nevertheless, little exploration has been made to achieve few-shot learning for the fact-checking task. However, fact-checking is an important problem, especially when the amount of information online is growing exponentially every day. In this paper, we propose a new way of utilizing the powerful transfer learning ability of a language model via a perplexity score. The most notable strength of our methodology lies in its capability in few-shot learning. With only two training samples, our methodology can already outperform the Major Class baseline by more than absolute 10% on the F1-Macro metric across multiple datasets. Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models. Moreover, we construct and publicly release two new fact-checking datasets related to COVID-19.
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
IIT Kharagpur（印度理工学院克勒格布尔分校）；Aditya English Medium School（阿迪蒂亚英语中学）；UCLA（加利福尼亚大学洛杉矶分校）；Stanford University（斯坦福大学）；University of South Carolina（南卡罗来纳大学）；Amazon GenAI（亚马逊生成式AI部门）
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning（湖北省人工智能与智慧学习重点实验室）；National Language Resources Monitoring and Research Center for Network Media（国家网络媒体语言资源监测与研究中心）；School of Computer, Central China Normal University（华中师范大学计算机学院）；School of Computer Science and Information Engineering, Hubei University（湖北大学计算机与信息工程学院）
Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models
School of Computing, Dublin City University（都柏林城市大学计算机学院）；Iran University of Science and Technology（伊朗科技大学）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Lucerne School of Computer Science and Information Technology（卢塞恩计算机科学与信息技术学院）
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
What is Wrong with Perplexity for Long-context Language Modeling?
State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（一般人工智能国家重点实验室，智能科学与技术学院，北京大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Alibaba Group（阿里巴巴集团）；TUM CIT, MCML, MDSI（技术大学（TUM） CIT、MCML、MDSI）；MIT EECS, CSAIL（麻省理工学院电子工程与计算机科学系，计算机科学与人工智能实验室）；Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）
CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
School of Cyber Science and Engineering, Sichuan University, China（四川大学网络科学与工程学院）；College of Computer Science, Sichuan University, China（四川大学计算机学院）；Institute of Data Science, National University of Singapore, Singapore（新加坡国立大学数据科学研究所）
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
School of Microelectronics, Southern University of Science and Technology（微电子学院，南方科技大学）；Department of Electrical and Electronic Engineering, University of Hong Kong（电气与电子工程系，香港大学）
One-Shot Learning as Instruction Data Prospector for Large Language Models
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）；The University of Sydney（悉尼大学）
CAST: Continuous and Differentiable Semi-Structured Sparsity-Aware Training for Large Language Models
Department of Computer Science and Technology, Institute for AI, BNRist Center, THBI Lab, Tsinghua-Bosch Joint ML Center, Tsinghua University（计算机科学与技术系、人工智能研究院、BNRist中心、THBI实验室、清华大学-博世联合机器学习中心、清华大学）
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
Yam Pak Charitable Foundation School of Computing and Information Sciences, Saint Francis University, Hong Kong（Yam Pak Charitable Foundation 计算与信息科学学院，圣弗朗西斯大学，香港）；Department of Applied Mathematics, Chung Yuan Christian University（应用数学系， Chung Yuan Christian University）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
Technische Universität Berlin（柏林工业大学）；Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）；National Institute of Informatics（信息学研究所）；Harz University of Applied Sciences（哈尔茨应用技术大学）
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Quality and Usability Lab, Technische Universität Berlin（柏林技术大学质量与可用性实验室）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Deutsche Telekom（德国电信）；Saarland Informatics Campus（萨尔州信息学校区）；Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）
