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.

The global proliferation of Generative Artificial Intelligence (GenAI) has led to the increasing presence of AI-generated text across a wide spectrum of topics, ranging from everyday content to critical and specialized domains. Often, individuals are unaware that the text they interact with was produced by AI systems rather than human authors, leading to instances where AI-generated content is unintentionally combined with human-written material. In response to this growing concern, we propose a novel approach as part of the Multi-Domain AI-Generated Text Detection (M-DAIGT) shared task, which aims to accurately identify AI-generated content across multiple domains, particularly in news reporting and academic writing. Given the rapid evolution of large language models (LLMs), distinguishing between human-authored and AI-generated text has become increasingly challenging. To address this, our method employs fine-tuning strategies using transformer-based language models for binary text classification. We focus on two specific domains, news and scholarly writing, and demonstrate that our approach, based on the DeBERTa transformer model, achieves superior performance in identifying AI-generated text. Our team, CNLP-NITS-PP, achieved 5th position in Subtask 1 and 3rd position in Subtask 2.