# AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification

arXiv: https://arxiv.org/abs/2505.11550 · HTML: https://mlsanity.com/paper/2505.11550/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 23 of 23)

- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [A Survey on Detection of LLMs-Generated Content](https://mlsanity.com/paper/2310.15654/) (2023)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases](https://mlsanity.com/paper/2308.09313/) (2023)
- [StyloAI: Distinguishing AI-Generated Content with Stylometric Analysis](https://mlsanity.com/paper/2405.10129/) (2024)
- [A Simple yet Efficient Ensemble Approach for AI-generated Text Detection](https://mlsanity.com/paper/2311.03084/) (2023)
- [Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors](https://mlsanity.com/paper/2312.12918/) (2023)
- [Detection of Machine-Generated Text: Literature Survey](https://mlsanity.com/paper/2402.01642/) (2024)
- [Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?](https://mlsanity.com/paper/2305.15908/) (2023)
- [Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text](https://mlsanity.com/paper/2401.09407/) (2024)
- [Artificial Text Detection with Multiple Training Strategies](https://mlsanity.com/paper/2212.05194/) (2022)
- [Distinguishing Chatbot from Human](https://mlsanity.com/paper/2408.04647/) (2024)

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

- [A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Features](https://mlsanity.com/paper/2511.21744/) (2025)
- [Assessing Classical Machine Learning and Transformer-based Approaches for Detecting AI-Generated Research Text](https://mlsanity.com/paper/2509.20375/) (2025)
- [Findings of the Counter Turing Test: AI-Generated Text Detection](https://mlsanity.com/paper/2605.20761/) (2026)

## Abstract (arXiv)

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.