Machine-generated Text Localization
Zhongping Zhang Wenda Qin Bryan A. Plummer
Machine-Generated Text (MGT) detection aims to identify a piece of text as
machine or human written. Prior work has primarily formulated MGT as a binary
classification task over an entire document, with limited work exploring cases
where only part of a document is machine generated. This paper provides the
first in-depth study of MGT that localizes the portions of a document that were
machine generated. Thus, if a bad actor were to change a key portion of a news
article to spread misinformation, whole document MGT detection may fail since
the vast majority is human written, but our approach can succeed due to its
granular approach. A key challenge in our MGT localization task is that short
spans of text, e.g., a single sentence, provides little information indicating
if it is machine generated due to its short length. To address this, we
leverage contextual information, where we predict whether multiple sentences
are machine or human written at once. This enables our approach to identify
changes in style or content to boost performance. A gain of 4-13% mean Average
Precision (mAP) over prior work demonstrates the effectiveness of approach on
five diverse datasets: GoodNews, VisualNews, WikiText, Essay, and WP. We
release our implementation at
\href{https://github.com/Zhongping-Zhang/MGT_Localization}{this http URL}.