# IMGTB: A Framework for Machine-Generated Text Detection Benchmarking

arXiv: https://arxiv.org/abs/2311.12574 · HTML: https://mlsanity.com/paper/2311.12574/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [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)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [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)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [Neural Deepfake Detection with Factual Structure of Text](https://mlsanity.com/paper/2010.07475/) (2020)
- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [Ludwig: a type-based declarative deep learning toolbox](https://mlsanity.com/paper/1909.07930/) (2019)

## Built on by (most cited first, 12 of 12)

- [Is Your Paper Being Reviewed by an LLM? Benchmarking AI Text Detection in Peer Review](https://mlsanity.com/paper/2502.19614/) (2025)
- [Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation](https://mlsanity.com/paper/2412.13666/) (2024)
- [Authorship Obfuscation in Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2401.07867/) (2024)
- [MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts](https://mlsanity.com/paper/2406.12549/) (2024)
- [Authorship Attribution in Multilingual Machine-Generated Texts](https://mlsanity.com/paper/2508.01656/) (2025)
- [mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection](https://mlsanity.com/paper/2506.01702/) (2025)
- [KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2402.13671/) (2024)
- [CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages](https://mlsanity.com/paper/2509.26051/) (2025)
- [Interpretable Predictability-Based AI Text Detection: A Replication Study](https://mlsanity.com/paper/2603.15034/) (2026)
- [mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code](https://mlsanity.com/paper/2604.21365/) (2026)
- [mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection](https://mlsanity.com/paper/2605.02695/) (2026)
- [mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection](https://mlsanity.com/paper/2605.02712/) (2026)

## Abstract (arXiv)

In the era of large language models generating high quality texts, it is a necessity to develop methods for detection of machine-generated text to avoid harmful use or simply due to annotation purposes. It is, however, also important to properly evaluate and compare such developed methods. Recently, a few benchmarks have been proposed for this purpose; however, integration of newest detection methods is rather challenging, since new methods appear each month and provide slightly different evaluation pipelines. In this paper, we present the IMGTB framework, which simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets. Its configurability and flexibility makes research and development of new detection methods easier, especially their comparison to the existing state-of-the-art detectors. The default set of analyses, metrics and visualizations offered by the tool follows the established practices of machine-generated text detection benchmarking found in state-of-the-art literature.