Large language models (LLMs) have reached human-like proficiency in
generating diverse textual content, underscoring the necessity for effective
fake text detection to avoid potential risks such as fake news in social media.
Previous research has mostly tested single models on in-distribution datasets,
limiting our understanding of how these models perform on different types of
data for LLM-generated text detection task. We researched this by testing five
specialized transformer-based models on both in-distribution and
out-of-distribution datasets to better assess their performance and
generalizability. Our results revealed that single transformer-based
classifiers achieved decent performance on in-distribution dataset but limited
generalization ability on out-of-distribution dataset. To improve it, we
combined the individual classifiers models using adaptive ensemble algorithms,
which improved the average accuracy significantly from 91.8% to 99.2% on an
in-distribution test set and from 62.9% to 72.5% on an out-of-distribution test
set. The results indicate the effectiveness, good generalization ability, and
great potential of adaptive ensemble algorithms in LLM-generated text
detection.

大语言模型生成文本内容的多样性接近于人类的能力，因此为了避免潜在的风险如社交媒体上的假新闻，需要有效的假文本检测。本研究通过在内部和外部分布数据集上测试五种专门的基于 Transformer 的模型来研究它们在 LLM 生成文本检测任务中的性能和泛化能力。结果表明，单个基于 Transformer 的分类器在内部数据集上取得了不错的性能，但在外部数据集上的泛化能力有限。为了改进这一点，我们使用自适应集成算法结合了个体分类器模型，将在内部测试集上的平均准确率从 91.8% 提高到 99.2%，在外部测试集上的准确率从 62.9% 提高到 72.5%。结果表明自适应集成算法在 LLM 生成文本检测中具有有效性、良好的泛化能力和巨大的潜力。