machine-generated-text

Continuously updated list of related resources for generative LLMs like GPT and their analysis and detection.

Uncertainty-gated two-stage AI-text detection with fast DTD routing and cross-family MS-LRC evidence. Sole-author submission to UncertaiNLP 2026 @ EMNLP.

Self-hostable AI text detector, bring your own model, calibrate on your domain, run locally or on Modal GPU.

MGTEval, an interactive platform for systematic evaluation of ai-generated text detectors, supporting 25+ detectors including DetectGPT, Fast-DetectGPT, Binoculars, and more.

Run CLIP queries against an image

Example dataset and prompt design of Korean Offensive language Machine Generation (K-OMG), published at IJCNLP-AACL 2023.

AI Text Feature Extractor for distinguishing AI-generated vs human-written content using 119+ linguistic features

Code for the publication of LREC'22

A measuring instrument for text, not an AI detector. Estimates statistical signals of machine-generated language and reports them with measured false-positive rates, broken down by text category. Abstains when the evidence doesn't hold. Never asserts authorship. Runs entirely locally; Spanish calibrated first.

📄 SemEval 2024 Task 8: Artificial Intelligence Text Detection System using Natural Language Processing and Neural Network techniques.

L3i++ at SemEval2024-task8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

Audits LLM pre-training corpora for synthetic-text contamination: explainable, calibrated scores, zero dependencies. Evaluated — catches older/open-weight model text well (AUC 0.92–0.99), frontier models poorly (0.61–0.64).

Attack-aware detection of AI-generated text (AACL-IJCNLP 2026): code, checkpoint and live demo

Linda - local AI-text detector for English, Russian and Polish: Linda Assay (full ensemble) and Linda Loupe (fast, CPU). Windows app, CLI, models on Hugging Face.

This repository is dedicated to a master class on translating texts into pdf format as part of the checkaihack hackathon

An awesome list of AI content detectors, datasets, benchmarks and research — plus the failure modes that matter.

Romanian multidomain human-machine dataset and detection of machine generated text

Testing whether current large language models still show the grammatical style differences from human writing identified in Reinhart et al. (2025), using their own chunk-continuation method.

A referee for AI-text detectors: measures them (including its own) at the operating points that decide whether someone gets accused, and publishes where they fail.

Repository for hosting the AC-IQuAD dataset: A Dataset of Indonesian Question Answering Automatically Constructed using Wikidata.