Omkar2118/datasets huggingface dataset:
RoFT-chatgpt

RoFT-chatgpt is a variation of RoFT dataset, where the same human prompts are continued with the gpt-3.5-turbo model.  Each dataset sample consists of ten sentences, with the first part written by a human and the remainder completed by an LLM. Consequently, every sample has a boundary indicating the index of the sentence where authorship changes.

Introducing paper: AI-generated text boundary detection with RoFT
TriBERT

TriBERT dataset consists of 12,049 training, 2,527 validation and 2,560 test Human-Machine collaborative texts. Each text contains both human-written and LLM-generated parts, which can appear in different orders (human → AI, AI → human). Therefore, each sample has between 1 and 3 boundaries, indicating the sentences where authorship changes. The texts were created using humanwritten essays with LLM-generated sections added using ChatGPT.

Introducing paper: Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education
TURINGBENCH

TuringBench is a benchmark environment that contains :

- Benchmark tasks- Turing Test (i.e., human vs. machine) and Authorship Attribution: (i.e., who is the author of this texts?)
- Datasets (Binary and Multi-class settings)
- Website with leaderboard

The dataset has 20 labels (19 AI text-generators and human). We built this dataset by collecting 10K news articles (mostly Politics) from sources like CNN and only keeping articles with 200-400 words. Next, we used the Titles of these human-written articles to prompt the AI text-generators (ex: GPT-2, GROVER, etc.) to generate 10K articles each. This gives us a sum total of 200K articles and 20 labels. However, since there are two benchmark tasks - Turing Test and Authorship Attribution settings, we have all 20 labels in one dataset for the multi-class setting and only human vs. one AI text-generator, making 19 binary-class datasets.

Introducing paper: TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
TweepFake

The TweepFake dataset consists of 25,572 social media messages posted either by bots or humans on Twitter. Each bot imitated a human account and was based on various generative techniques, including Markov Chains, RNN, RNN+Markov, LSTM, and GPT-2.

Introducing paper: TweepFake: about Detecting Deepfake Tweets
MixSet
Full name: Mixcase Dataset

MIXSET comprises a total of 3.6k mixtext instances. The dataset features a blend of HWT(human-written text) and MGT(machine-generated text).

Introducing paper: LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
MAGE

The MAGE dataset provides a large set of generated texts using 27 LLMs from seven different groups: OpenAI GPT, LLaMA, GLM130B, FLAN-T5, OPT, BigScience, and EleutherAI. In total, the dataset contains 432,682 texts, along with two additional sets. The first is an additional test set with texts from unseen domains generated by an unseen model, namely GPT-4. The second set is designed to evaluate the robustness of detectors against paraphrasing attacks. To achieve this, the GPT-3.5-turbo model was employed to paraphrase the sentences from the first set, with all paraphrased texts treated as machine-generated.

Introducing paper: MAGE: Machine-generated Text Detection in the Wild
Ghostbuster

The Ghostbusters dataset leverages the GPT-3.5-turbo model for generating texts in the domains of creative writing, news, and student essays, providing 2,000 texts in the first two domains and 1,994 in the latter.

Introducing paper: Ghostbuster: Detecting Text Ghostwritten by Large Language Models