# A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Features

arXiv: https://arxiv.org/abs/2511.21744 · HTML: https://mlsanity.com/paper/2511.21744/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://mlsanity.com/paper/2311.07914/) (2023)
- [DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning](https://mlsanity.com/paper/2410.20964/) (2024)
- [Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains](https://mlsanity.com/paper/2309.11285/) (2023)
- [Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?](https://mlsanity.com/paper/2306.05871/) (2023)
- [Robust AI-Generated Text Detection by Restricted Embeddings](https://mlsanity.com/paper/2410.08113/) (2024)
- [StyloAI: Distinguishing AI-Generated Content with Stylometric Analysis](https://mlsanity.com/paper/2405.10129/) (2024)
- [Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis](https://mlsanity.com/paper/2304.05534/) (2023)
- [TextDescriptives: A Python package for calculating a large variety of metrics from text](https://mlsanity.com/paper/2301.02057/) (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)
- [AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification](https://mlsanity.com/paper/2505.11550/) (2025)
- [Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts](https://mlsanity.com/paper/2404.08673/) (2024)

## Built on by (most cited first, 5 of 5)

- [Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models](https://mlsanity.com/paper/2604.19768/) (2026)
- [How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework](https://mlsanity.com/paper/2605.23651/) (2026)
- [Relational Over-Regularization: Graph-Based AI-Generated Text Detection via Sentence Transition Deviation](https://mlsanity.com/paper/2608.26694/) (2026)
- [AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths](https://mlsanity.com/paper/2609.06771/) (2026)
- [Writerslogic at PAN 2026: Process over Content for Robust Detection under Domain Shift](https://mlsanity.com/paper/2610.03565/) (2026)

## Abstract (arXiv)

A growing number of AI-generated texts raise serious concerns. Most existing approaches to AI-generated text detection rely on fine-tuning large transformer models or building ensembles, which are computationally expensive and often provide limited generalization across domains. Existing lightweight alternatives achieved significantly lower accuracy on large datasets. We introduce NEULIF, a lightweight approach that achieves best performance in the lightweight detector class, that does not require extensive computational power and provides high detection accuracy. In our approach, a text is first decomposed into stylometric and readability features which are then used for classification by a compact Convolutional Neural Network (CNN) or Random Forest (RF). Evaluated and tested on the Kaggle AI vs. Human corpus, our models achieve 97% accuracy (~ 0.95 F1) for CNN and 95% accuracy (~ 0.94 F1) for the Random Forest, demonstrating high precision and recall, with ROC-AUC scores of 99.5% and 95%, respectively. The CNN (~ 25 MB) and Random Forest (~ 10.6 MB) models are orders of magnitude smaller than transformer-based ensembles and can be run efficiently on standard CPU devices, without sacrificing accuracy. This study also highlights the potential of such models for broader applications across languages, domains, and streaming contexts, showing that simplicity, when guided by structural insights, can rival complexity in AI-generated content detection.