AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908368575135744 |
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| author | Abburi, Harika Bhattacharya, Sanmitra Bowen, Edward Pudota, Nirmala |
| author_facet | Abburi, Harika Bhattacharya, Sanmitra Bowen, Edward Pudota, Nirmala |
| contents | Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across a wide range of styles and genres. However, such capabilities are prone to potential misuse, such as fake news generation, spam email creation, and misuse in academic assignments. As a result, accurate detection of AI-generated text and identification of the model that generated it are crucial for maintaining the responsible use of LLMs. In this work, we addressed two sub-tasks put forward by the Defactify workshop under AI-Generated Text Detection shared task at the Association for the Advancement of Artificial Intelligence (AAAI 2025): Task A involved distinguishing between human-authored or AI-generated text, while Task B focused on attributing text to its originating language model. For each task, we proposed two neural architectures: an optimized model and a simpler variant. For Task A, the optimized neural architecture achieved fifth place with $F1$ score of 0.994, and for Task B, the simpler neural architecture also ranked fifth place with $F1$ score of 0.627. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_11550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification Abburi, Harika Bhattacharya, Sanmitra Bowen, Edward Pudota, Nirmala Computation and Language Artificial Intelligence Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across a wide range of styles and genres. However, such capabilities are prone to potential misuse, such as fake news generation, spam email creation, and misuse in academic assignments. As a result, accurate detection of AI-generated text and identification of the model that generated it are crucial for maintaining the responsible use of LLMs. In this work, we addressed two sub-tasks put forward by the Defactify workshop under AI-Generated Text Detection shared task at the Association for the Advancement of Artificial Intelligence (AAAI 2025): Task A involved distinguishing between human-authored or AI-generated text, while Task B focused on attributing text to its originating language model. For each task, we proposed two neural architectures: an optimized model and a simpler variant. For Task A, the optimized neural architecture achieved fifth place with $F1$ score of 0.994, and for Task B, the simpler neural architecture also ranked fifth place with $F1$ score of 0.627. |
| title | AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.11550 |