This paper presents the MasonTigers entry to the SemEval-2024 Task 8 -
Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text
Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text
Classification (Track A), Multi-Way Machine-Generated Text Classification
(Track B), and Human-Machine Mixed Text Detection (Track C). Our best
performing approaches utilize mainly the ensemble of discriminator transformer
models along with sentence transformer and statistical machine learning
approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of
FLAN-T5 are used for Track A and B.

这篇论文介绍了 MasonTigers 参与 SemEval-2024 任务 8，即多生成器、多域和多语言黑盒机器生成文本检测。该任务包括二进制的人工书写 vs. 机器生成文本分类（A 轨道），多种机器生成文本分类（B 轨道）和人机混合文本检测（C 轨道）。我们最好的方法主要使用鉴别器转换模型的集成，以及句子转换模型和统计机器学习方法在特定情况下。此外，对于 A 轨道和 B 轨道，还使用了零样本提示和 FLAN-T5 的微调。

MasonTigers 在 SemEval-2024 第 8 任务上的性能分析：基于 Transformer 模型的机器生成文本检测

MasonTigers at SemEval-2024 Task 8: Performance Analysis of  Transformer-based Models on Machine-Generated Text Detection

The advent of Large Language Models (LLMs) has brought an unprecedented surge
in machine-generated text (MGT) across diverse channels. This raises legitimate
concerns about its potential misuse and societal implications. The need to
identify and differentiate such content from genuine human-generated text is
critical in combating disinformation, preserving the integrity of education and
scientific fields, and maintaining trust in communication. In this work, we
address this problem by introducing a new benchmark involving multilingual,
multi-domain and multi-generator for MGT detection -- M4GT-Bench. It is
collected for three task formulations: (1) mono-lingual and multi-lingual
binary MGT detection; (2) multi-way detection identifies which particular model
generates the text; and (3) human-machine mixed text detection, where a word
boundary delimiting MGT from human-written content should be determined. Human
evaluation for Task 2 shows less than random guess performance, demonstrating
the challenges to distinguish unique LLMs. Promising results always occur when
training and test data distribute within the same domain or generators.

通过引入多语言、多领域和多发生器的机器生成文本检测基准 M4GT-Bench，本研究解决了识别和区分机器生成文本与人类生成文本的问题，包括单语和多语二元机器生成文本检测、多类别检测以确定生成文本具体使用的模型以及在人机混合文本中确定生成文本与人类编写内容之间的边界的挑战。