Domain Gating Ensemble Networks for AI-Generated Text Detection
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arXiv
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| Format: | Preprint |
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2025
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| author | Tripathi, Arihant Dugan, Liam Gao, Charis Huan, Maggie Jin, Emma Zhang, Peter Zhang, David Zhao, Julia Callison-Burch, Chris |
| author_facet | Tripathi, Arihant Dugan, Liam Gao, Charis Huan, Maggie Jin, Emma Zhang, Peter Zhang, David Zhao, Julia Callison-Burch, Chris |
| contents | As state-of-the-art language models continue to improve, the need for robust detection of machine-generated text becomes increasingly critical. However, current state-of-the-art machine text detectors struggle to adapt to new unseen domains and generative models. In this paper we present DoGEN (Domain Gating Ensemble Networks), a technique that allows detectors to adapt to unseen domains by ensembling a set of domain expert detector models using weights from a domain classifier. We test DoGEN on a wide variety of domains from leading benchmarks and find that it achieves state-of-the-art performance on in-domain detection while outperforming models twice its size on out-of-domain detection. We release our code and trained models to assist in future research in domain-adaptive AI detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13855 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain Gating Ensemble Networks for AI-Generated Text Detection Tripathi, Arihant Dugan, Liam Gao, Charis Huan, Maggie Jin, Emma Zhang, Peter Zhang, David Zhao, Julia Callison-Burch, Chris Computation and Language Artificial Intelligence Machine Learning As state-of-the-art language models continue to improve, the need for robust detection of machine-generated text becomes increasingly critical. However, current state-of-the-art machine text detectors struggle to adapt to new unseen domains and generative models. In this paper we present DoGEN (Domain Gating Ensemble Networks), a technique that allows detectors to adapt to unseen domains by ensembling a set of domain expert detector models using weights from a domain classifier. We test DoGEN on a wide variety of domains from leading benchmarks and find that it achieves state-of-the-art performance on in-domain detection while outperforming models twice its size on out-of-domain detection. We release our code and trained models to assist in future research in domain-adaptive AI detection. |
| title | Domain Gating Ensemble Networks for AI-Generated Text Detection |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.13855 |