4 papers
Identifying Bias in Machine-generated Text Detection
Kevin Stowe, Svetlana Afanaseva, Rodolfo Raimundo +2
The meteoric rise in text generation capability has been accompanied by parallel growth in interest in machine-generated text detection: the capability to identify whether a given…
Spotlights and Blindspots: Evaluating Machine-Generated Text Detection
Kevin Stowe, Kailash Patil
With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, but inconsistent datasets, eva…
Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing
Shafiuddin Rehan Ahmed, Zhiyong Eric Wang, George Arthur Baker +2
The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferrin…
Identifying Fairness Issues in Automatically Generated Testing Content
Kevin Stowe, Benny Longwill, Alyssa Francis +3
Natural language generation tools are powerful and effective for generating content. However, language models are known to display bias and fairness issues, making them impractical…