# Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm

arXiv: https://arxiv.org/abs/2405.06652 · HTML: https://mlsanity.com/paper/2405.06652/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators](https://mlsanity.com/paper/2309.14279/) (2023)

## Built on by (most cited first, 23 of 23)

- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset](https://mlsanity.com/paper/2406.13113/) (2024)
- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning](https://mlsanity.com/paper/2505.16270/) (2025)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions](https://mlsanity.com/paper/2406.06081/) (2024)
- [Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising](https://mlsanity.com/paper/2406.10239/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement](https://mlsanity.com/paper/2508.04289/) (2025)
- [Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models](https://mlsanity.com/paper/2406.17992/) (2024)
- [Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System](https://mlsanity.com/paper/2407.02759/) (2024)
- [Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model](https://mlsanity.com/paper/2410.09923/) (2024)
- [Detecting and Classifying Defective Products in Images Using YOLO](https://mlsanity.com/paper/2412.16935/) (2024)
- [Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications](https://mlsanity.com/paper/2507.12945/) (2025)
- [Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?](https://mlsanity.com/paper/2507.21055/) (2025)
- [Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making](https://mlsanity.com/paper/2407.11006/) (2024)
- [Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMs](https://mlsanity.com/paper/2604.02230/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)
- [Latent Trajectory Discrimination for AI-Generated Text Detection](https://mlsanity.com/paper/2607.14967/) (2026)

## Abstract (arXiv)

In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research. Firstly the text is Unicode normalised, converted to lowercase form, characters other than non-alphabetic characters and punctuation marks are removed by regular expressions, spaces are added around punctuation marks, first and last spaces are removed, consecutive ellipses are replaced with single spaces and the text is connected using the specified delimiter. Next remove non-alphabetic characters and extra whitespace characters, replace multiple consecutive whitespace characters with a single space and again convert to lowercase form. The deep learning model combines layers such as LSTM, Transformer and CNN for text classification or sequence labelling tasks. The training and validation sets show that the model loss decreases from 0.127 to 0.005 and accuracy increases from 94.96 to 99.8, indicating that the model has good detection and classification ability for AI generated text. The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0.99, a recall of 1, and an f1 score of 0.99, achieving a very high classification accuracy. Looking forward, it has the prospect of wide application in the field of AI text detection.