面向 AI 生成文本的鲁棒细粒度检测模型研究
Robust and Fine-Grained Detection of AI Generated Texts
研究者推出一组用于 token 分类的模型，可检测 LLM 生成及人机共写文本，在未见领域、未见生成器、非母语文本和对抗输入上均表现良好。模型基于一个包含 240 万以上文本的新数据集训练，覆盖 23 种语言、多由主流闭源 LLM 共同创作。论文还报告了模型在各领域与生成器上的表现，以及对抗方法、文本长度等因素的影响。
Authors:Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen, Drishti Sharma, Siddhant Gupta, Jebish Purbey, Ashay Srivastava, Subhasya TippaReddy, Arvind Reddy Bobbili, Suraj Telugara Chandrashekhar, Ankan Ghosh, Modabbir Adeeb, Srinadh Vura, Suman Debnath, Hamza Farooq
Abstract:An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existence day by day. Existing systems often struggle with accurately identifying AI-generated content over shorter texts. Further, not all texts might be entirely authored by a human or LLM, hence we focused more over partial cases i.e human-LLM co-authored texts. Our paper introduces a set of models built for the task of token classification which are trained on an extensive collection of human-machine co-authored texts, which performed well over texts of unseen domains, unseen generators, texts by non-native speakers and those with adversarial inputs. We also introduce a new dataset of over 2.4M such texts mostly co-authored by several popular proprietary LLMs over 23 languages. We also present findings of our models' performance over each texts of each domain and generator. Additional findings include comparison of performance against each adversarial method, length of input texts and characteristics of generated texts compared to the original human authored texts.
| Comments: | 18 pages, 6 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2504.11952 [cs.CL] |
| (or arXiv:2504.11952v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2504.11952 arXiv-issued DOI via DataCite |
Submission history
From: Ram Mohan Rao Kadiyala [view email]
[v1]
Wed, 16 Apr 2025 10:29:30 UTC (7,712 KB)
[v2]
Thu, 22 May 2025 10:09:18 UTC (8,739 KB)
[v3]
Thu, 31 Jul 2025 09:14:25 UTC (7,480 KB)
[v4]
Sat, 24 Oct 2026 01:05:00 UTC (7,473 KB)
来源：arXiv cs.AI · arxiv.org