# RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text

arXiv: https://arxiv.org/abs/2010.03070 · HTML: https://mlsanity.com/paper/2010.03070/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 30 of 36)

- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Hurdles to Progress in Long-form Question Answering](https://mlsanity.com/paper/2103.06332/) (2021)
- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://mlsanity.com/paper/2107.01294/) (2021)
- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://mlsanity.com/paper/2210.07321/) (2022)
- [Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text](https://mlsanity.com/paper/2212.12672/) (2022)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2402.11175/) (2024)
- [Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective](https://mlsanity.com/paper/2210.10488/) (2022)
- [Few-Shot Detection of Machine-Generated Text using Style Representations](https://mlsanity.com/paper/2401.06712/) (2024)
- [People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text](https://mlsanity.com/paper/2501.15654/) (2025)
- [Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian](https://mlsanity.com/paper/2206.01583/) (2022)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [AI vs. Human -- Differentiation Analysis of Scientific Content Generation](https://mlsanity.com/paper/2301.10416/) (2023)
- [Creating a Large Language Model of a Philosopher](https://mlsanity.com/paper/2302.01339/) (2023)
- [Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?](https://mlsanity.com/paper/2304.01002/) (2023)
- [AI-generated text boundary detection with RoFT](https://mlsanity.com/paper/2311.08349/) (2023)
- [A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications](https://mlsanity.com/paper/2202.02013/) (2022)
- [Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text](https://mlsanity.com/paper/2309.07689/) (2023)
- [GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge](https://mlsanity.com/paper/2501.08913/) (2025)
- [DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution](https://mlsanity.com/paper/2512.04838/) (2025)
- [DIALOG-22 RuATD Generated Text Detection](https://mlsanity.com/paper/2206.08029/) (2022)
- [Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2401.12326/) (2024)
- [Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems](https://mlsanity.com/paper/2503.04945/) (2025)
- [Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation](https://mlsanity.com/paper/2509.17830/) (2025)
- [Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code](https://mlsanity.com/paper/2204.13848/) (2022)
- [Synthetic Text Detection: Systemic Literature Review](https://mlsanity.com/paper/2210.06336/) (2022)

## Abstract (arXiv)

In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.