The proliferation of large language models (LLMs) has significantly enhanced
text generation capabilities across various industries. However, these models'
ability to generate human-like text poses substantial challenges in discerning
between human and AI authorship. Despite the effectiveness of existing
AI-generated text detectors, their development is hindered by the lack of
comprehensive, publicly available benchmarks. Current benchmarks are limited to
specific scenarios, such as question answering and text polishing, and
predominantly focus on English texts, failing to capture the diverse
applications and linguistic nuances of LLMs. To address these limitations, this
paper constructs a comprehensive bilingual benchmark in both Chinese and
English to evaluate mainstream AI-generated text detectors. We categorize LLM
text generation into five distinct operations: Create, Update, Delete, Rewrite,
and Translate (CUDRT), encompassing all current LLMs activities. We also
establish a robust benchmark evaluation framework to support scalable and
reproducible experiments. For each CUDRT category, we have developed extensive
datasets to thoroughly assess detector performance. By employing the latest
mainstream LLMs specific to each language, our datasets provide a thorough
evaluation environment. Extensive experimental results offer critical insights
for optimizing AI-generated text detectors and suggest future research
directions to improve detection accuracy and generalizability across various
scenarios.

本文构建了一个综合的中英文双语标杆来评估主流的 AI 生成文本检测器，并将 LLM 文本生成划分为创建、更新、删除、重写和翻译五个不同的操作，为各种场景提供优化 AI 生成文本检测器的关键洞察和改进方向。