Can We Trust LLM Detectors?
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908790011461632 |
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| author | Sandhan, Jivnesh Jaiswal, Harshit Cheng, Fei Murawaki, Yugo |
| author_facet | Sandhan, Jivnesh Jaiswal, Harshit Cheng, Fei Murawaki, Yugo |
| contents | The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training-free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in-domain, they degrade sharply out-of-domain, and training-free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain-agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15301 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can We Trust LLM Detectors? Sandhan, Jivnesh Jaiswal, Harshit Cheng, Fei Murawaki, Yugo Computation and Language Artificial Intelligence The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training-free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in-domain, they degrade sharply out-of-domain, and training-free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain-agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI |
| title | Can We Trust LLM Detectors? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.15301 |