对比三类方法，检测大模型生成文本的准确率与鲁棒性。
Robust Detection of LLM-Generated Text: A Comparative Analysis
- 用传统机器学习、BERT和大模型三类方法检测生成文本。
- 发现基于大模型的检测器在跨数据集上泛化能力更强。
- 适合关注内容安全与反伪造的研究者与平台方。
大语言模型生成复杂文本的能力使其广泛应用于生活各领域，其输出可能迅速占据网络资源。随着大模型影响扩大，开发高效文本检测器至关重要，以防止技术滥用并保护社交媒体免受虚假内容侵害。本文旨在判断文本是否由大模型生成，属于基础二分类任务。我们采用三类开源数据集上的不同分类方法：传统机器学习（如逻辑回归、k均值聚类、高斯朴素贝叶斯、支持向量机），基于转换器的方法（如BERT），以及利用大模型自身进行检测的算法。重点考察模型泛化能力、潜在对抗攻击下的稳定性及评估准确性。最后总结当前实验结果，并提出未来研究方向。
原文摘要 · Abstract (English)
The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becomes increasingly important to develop powerful detectors for the generated text. This detector is essential to prevent the potential misuse of these technologies and to protect areas such as social media from the negative effects of false content generated by LLMS. The main goal of LLM-generated text detection is to determine whether text is generated by an LLM, which is a basic binary classification task. In our work, we mainly use three different classification methods based on open source datasets: traditional machine learning techniques such as logistic regression, k-means clustering, Gaussian Naive Bayes, support vector machines, and methods based on converters such as BERT, and finally algorithms that use LLMs to detect LLM-generated text. We focus on model generalization, potential adversarial attacks, and accuracy of model evaluation. Finally, the possible research direction in the future is proposed, and the current experimental results are summarized.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。