Nowadays, powerful large language models (LLMs) such as ChatGPT have demonstrated revolutionary power in a variety of tasks. Consequently, the detection of machine-generated texts (MGTs) is becoming increasingly crucial as LLMs become more advanced and prevalent. These models have the ability to generate human-like language, making it challenging to discern whether a text is authored by a human or a machine. This raises concerns regarding authenticity, accountability, and potential bias. However, existing methods for detecting MGTs are evaluated using different model architectures, datasets, and experimental settings, resulting in a lack of a comprehensive evaluation framework that encompasses various methodologies. Furthermore, it remains unclear how existing detection methods would perform against powerful LLMs. In this paper, we fill this gap by proposing the first benchmark framework for MGT detection against powerful LLMs, named MGTBench. Extensive evaluations on public datasets with curated texts generated by various powerful LLMs such as ChatGPT-turbo and Claude demonstrate the effectiveness of different detection methods. Our ablation study shows that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples. Moreover, we delve into a more challenging task: text attribution. Our findings indicate that the model-based detection methods still perform well in the text attribution task. To investigate the robustness of different detection methods, we consider three adversarial attacks, namely paraphrasing, random spacing, and adversarial perturbations. We discover that these attacks can significantly diminish detection effectiveness, underscoring the critical need for the development of more robust detection methods.
Towards Benchmarking Foundation Models for Tabular Data With Text
Department of Computer Science, University of Freiburg, Freiburg, Germany（弗赖堡大学计算机科学系）；Prior Labs, Freiburg, Germany（Prior实验室）；ELLIS Institute, Tubingen, Germany（图宾根ELLIS研究所）
MMSciBench: Benchmarking Language Models on Chinese Multimodal Scientific Problems
Department of Computer Science, Yale University（耶鲁大学计算机科学系）；Department of Computer Science, Brown University（布朗大学计算机科学系）；School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Key Laboratory of Data Science（上海数据科学关键实验室）；Datawiz LLC
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Technion – Israel Institute of Technology（技术ion – 以色列理工学院）；Prior Labs（Prior实验室）；NVIDIA；SODA Team, INRIA Saclay, Palaiseau（SODA团队，INRIA萨克莱，帕莱索）；University of Freiburg（弗赖堡大学）；Probabl；ELLIS Institute Tübingen（图宾根ELLIS研究所）
DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Hengxin Tech（恒心科技）
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD与CG国家重点实验室）；UESTC；University of Virginia（弗吉尼亚大学）；HKUST(GZ)（香港科技大学（广州））；Cornell University（康奈尔大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）
