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

TL;DR

RoFT is an interactive tool that assesses human ability to detect machine-generated text, providing insights into human perception and evaluation of NLG systems across different domains.

Contribution

The paper introduces RoFT, a novel platform for evaluating human detection of machine-generated text and a new task focusing on identifying transition boundaries in text.

Findings

Preliminary results show varying human detection accuracy.

RoFT enables analysis of perception differences across domains.

The tool facilitates future research in NLG evaluation.

Abstract

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.