4 papers
Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
Gerrit Quaremba, Hanqi Yan, Elizabeth Black +2
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade o…
WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia
Gerrit Quaremba, Elizabeth Black, Denny VrandeÄiÄ +1
Given Wikipedia's role as a trusted source of high-quality, reliable content, concerns are growing about the proliferation of low-quality machine-generated text (MGT) produced by l…
Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages
Gerrit Quaremba, Amy Rechkemmer, Elizabeth Black +2
In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, this task instantiates as Cit…
TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices
Gerrit Quaremba, Elizabeth Black, Denny VrandeÄiÄ +1
Automatically detecting machine-generated text (MGT) is critical to maintaining the knowledge integrity of user-generated content (UGC) platforms such as Wikipedia. Existing detect…