aiqbench analysis
This paper proposes a discrete-class framework for text provenance that grades AI involvement in a document, instead of relying on a single human-to-AI score. Its main finding is that adversarial paraphrasing substantially degrades detectors trained only on benign data, and that augmenting training with adversarial paraphrases restores robustness and transfers across generators. Readers building or evaluating AI-text detectors should note that the reported gains come from experiments on specific datasets and generation models, so cross-domain performance remains to be seen.
Towards Fine-grained and Robust Detection of Human-AI Co-authorship
arXiv:2611.01427v1 Announce Type: cross Abstract: The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complicating text provenance, and raising concerns for misinformation, plagiarism, and intellectual property. While prior work on AI-generated text detection has progressed, one-dimensional human-AI co-authorship scores lack signals directly relevant to ``AI involvement''. It remains unclear how adversarial paraphrasing distorts detection signals. In this work, we formulate a discrete-class text provenance detection framework. This framework allows an abstraction of realistic co-authorship scenarios into distinct degrees of AI involvement, differentiating the AI polishing on the surface level from the AI updating the discourse structures. This framework reveals the mechanisms of how adversarial paraphrasing blurs the decision boundaries for co-authorship detectors. To improve robustness of the detectors, we augment training data with adversarial paraphrases generated by the state-of-the-art paraphrasing attack model, exposing detectors to challenging meaning-preserving rewrites. Extensive experiments across multiple datasets and generation models demonstrate that adversarial paraphrasing substantially degrades detectors trained only on benign data, while adversarial data augmentation leads to consistent robustness gains and good cross-generator transferability. Overall, our findings highlight the value of discrete-class formulation and principled data augmentation for building reliable detectors of human-AI co-authorship.
Abstract:The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complicating text provenance, and raising concerns for misinformation, plagiarism, and intellectual property. While prior work on AI-generated text detection has progressed, one-dimensional human-AI co-authorship scores lack signals directly relevant to ``AI involvement''. It remains unclear how adversarial paraphrasing distorts detection signals. In this work, we formulate a discrete-class text provenance detection framework. This framework allows an abstraction of realistic co-authorship scenarios into distinct degrees of AI involvement, differentiating the AI polishing on the surface level from the AI updating the discourse structures. This framework reveals the mechanisms of how adversarial paraphrasing blurs the decision boundaries for co-authorship detectors. To improve robustness of the detectors, we augment training data with adversarial paraphrases generated by the state-of-the-art paraphrasing attack model, exposing detectors to challenging meaning-preserving rewrites. Extensive experiments across multiple datasets and generation models demonstrate that adversarial paraphrasing substantially degrades detectors trained only on benign data, while adversarial data augmentation leads to consistent robustness gains and good cross-generator transferability. Overall, our findings highlight the value of discrete-class formulation and principled data augmentation for building reliable detectors of human-AI co-authorship.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2611.01427 [cs.CL]
(or arXiv:2611.01427v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.01427 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.18653/2026.findings-emnlp.1365 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 300 words