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GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content

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arxiv 2305.07969 v2 pith:JTQC6UHD submitted 2023-05-13 cs.CL

Yutian Chen , Hao Kang , Vivian Zhai , Liangze Li , Rita Singh , Bhiksha Raj This is my paper
classification cs.CL
keywords modelstextgeneratedapproachchatgptchatgpt-generatedcontentdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed dataset named OpenGPTText, which consists of rephrased content generated using ChatGPT. We then designed, implemented, and trained two different models for text classification, using Robustly Optimized BERT Pretraining Approach (RoBERTa) and Text-to-Text Transfer Transformer (T5), respectively. Our models achieved remarkable results, with an accuracy of over 97% on the test dataset, as evaluated through various metrics. Furthermore, we conducted an interpretability study to showcase our model's ability to extract and differentiate key features between human-written and ChatGPT-generated text. Our findings provide important insights into the effective use of language models to detect generated text.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Detecting LLM-generated Code with Subtle Modification by Adversarial Training

    cs.SE 2025-07 conditional novelty 6.0 of 10

    CodeGPTSensor+, trained with adversarial samples that combine identifier renaming and structure transformation, is substantially more robust to subtle modifications of LLM-generated code than the original CodeGPTSensor.

  2. Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Human involvement in AI-generated academic text can be estimated continuously by training a RoBERTa regressor on BERTScore-derived labels, outperforming binary detectors on a new synthetic dataset.

  3. DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text

    cs.CL 2026-08 conditional novelty 4.0 of 10

    A fine-tuned DeBERTa-v3 classifier reportedly beats a RoBERTa baseline at detecting paraphrased AI text (97.53% accuracy, 99.53% AUC), but sample-count mismatches and a possible train/test content overlap make the exa...

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