# Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models

arXiv: https://arxiv.org/abs/2502.16857 · HTML: https://mlsanity.com/paper/2502.16857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Data Noising as Smoothing in Neural Network Language Models](https://mlsanity.com/paper/1703.02573/) (2017)
- [Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation](https://mlsanity.com/paper/1902.01509/) (2019)

## Built on by (most cited first, 3 of 3)

- [Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI](https://mlsanity.com/paper/2512.06922/) (2025)
- [Findings of the Counter Turing Test: AI-Generated Text Detection](https://mlsanity.com/paper/2605.20761/) (2026)
- [DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text](https://mlsanity.com/paper/2608.01046/) (2026)

## Abstract (arXiv)

This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying the specific large language model that generated the text. Our team (Sarang) achieved the 1st place in both tasks with F1 scores of 1.0 and 0.9531, respectively. The methodology involves adding noise to the dataset to improve model robustness and generalization. We used an ensemble of DeBERTa models to effectively capture complex patterns in the text. The result indicates the effectiveness of our noise-driven and ensemble-based approach, setting a new standard in AI-generated text detection and providing guidance for future developments.