Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation

• URL: http://arxiv.org/abs/2509.17830v2
• Date: Tue, 23 Sep 2025 03:46:06 GMT
• Title: Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation
• Authors: Lekkala Sai Teja, Annepaka Yadagiri, Partha Pakray, Chukhu Chunka, Mangadoddi Srikar Vardhan,
• Abstract summary: A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text.<n>Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs)<n>The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts.
• Score: 3.088244520495001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generation of Artificial Intelligence (AI) texts in important works has become a common practice that can be used to misuse and abuse AI at various levels. Traditional AI detectors often rely on document-level classification, which struggles to identify AI content in hybrid or slightly edited texts designed to avoid detection, leading to concerns about the model's efficiency, which makes it hard to distinguish between human-written and AI-generated texts. A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text, leveraging nuanced linguistic signals overlooked by document-level classifiers. By this method, detecting and segmenting AI and human-written text within a single document at the token-level granularity is achieved. Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs). This approach extends the power of transformers to extract semantic and syntactic patterns, and the neural network component to capture enhanced sequence-level representations, thereby improving the boundary predictions by the CRF layer, which enhances sequence recognition and further identification of the partition between Human- and AI-generated texts. The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts. Our experimental comparisons are with zero-shot detectors and the existing state-of-the-art models, along with rigorous ablation studies to justify that this approach, in particular, can accurately detect the spans of AI texts in a completely collaborative text. All our source code and the processed datasets are available in our GitHub repository.

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