Recent advances in natural language processing (NLP) have led to the
development of large language models (LLMs) such as ChatGPT. This paper
proposes a methodology for developing and evaluating ChatGPT detectors for
French text, with a focus on investigating their robustness on out-of-domain
data and against common attack schemes. The proposed method involves
translating an English dataset into French and training a classifier on the
translated data. Results show that the detectors can effectively detect
ChatGPT-generated text, with a degree of robustness against basic attack
techniques in in-domain settings. However, vulnerabilities are evident in
out-of-domain contexts, highlighting the challenge of detecting adversarial
text. The study emphasizes caution when applying in-domain testing results to a
wider variety of content. We provide our translated datasets and models as
open-source resources. this https URL

该论文提出了一种用于开发和评估 ChatGPT 检测器的方法，重点研究它们对领域外数据和常见攻击方案的鲁棒性，结果显示在域内环境中，检测器可以有效地检测 ChatGPT 生成的文本，但在域外环境中存在容易受到攻击的弱点.

面向语言模型生成文本的鲁棒检测：ChatGPT 是否易于检测？

Towards a Robust Detection of Language Model Generated Text: Is ChatGPT  that Easy to Detect?

Recent advances in language modeling have led to computationally intensive
and resource-demanding state-of-the-art models. In an effort towards
sustainable practices, we study the impact of pre-training data volume on
compact language models. Multiple BERT-based models are trained on gradually
increasing amounts of French text. Through fine-tuning on the French Question
Answering Dataset (FQuAD), we observe that well-performing models are obtained
with as little as 100 MB of text. In addition, we show that past critically low
amounts of pre-training data, an intermediate pre-training step on the
task-specific corpus does not yield substantial improvements.

我们研究了预训练数据量对紧凑型语言模型的影响。我们发现，即使只有 100MB 的预训练数据，通过在法语问答数据集（FQuAD）上微调 BERT 模型，都可以得到表现很好的模型。此外，我们还展示了，除了任务特定的语料库，中等数量的预训练数据并不能取得实质性的改进。