12 citations · 22 across the 9 of their papers we have counts for
8 papers · 1 filter
A Platform for Investigating Public Health Content with Efficient Concern Classification
Christopher Li, Rickard Stureborg, Bhuwan Dhingra +1
A recent rise in online content expressing concerns with public health initiatives has contributed to already stalled uptake of preemptive measures globally. Future public health e…
MatViX: Multimodal Information Extraction from Visually Rich Articles
Ghazal Khalighinejad, Sharon Scott, Ollie Liu +4
Multimodal information extraction (MIE) is crucial for scientific literature, where valuable data is often spread across text, figures, and tables. In materials science, extracting…
Your Large Language Models Are Leaving Fingerprints
Hope McGovern, Rickard Stureborg, Yoshi Suhara +1
It has been shown that finetuned transformers and other supervised detectors effectively distinguish between human and machine-generated text in some situations arXiv:2305.13242, b…
Large Language Models are Inconsistent and Biased Evaluators
Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara
The zero-shot capability of Large Language Models (LLMs) has enabled highly flexible, reference-free metrics for various tasks, making LLM evaluators common tools in NLP. However,…
Tailoring Vaccine Messaging with Common-Ground Opinions
Rickard Stureborg, Sanxing Chen, Ruoyu Xie +6
One way to personalize chatbot interactions is by establishing common ground with the intended reader. A domain where establishing mutual understanding could be particularly impact…
Hierarchical Multi-Label Classification of Online Vaccine Concerns
Chloe Qinyu Zhu, Rickard Stureborg, Bhuwan Dhingra
Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic. Identifying longitudinal trends in vaccine concerns and misinformation mig…
Do Not Harm Protected Groups in Debiasing Language Representation Models
Chloe Qinyu Zhu, Rickard Stureborg, Brandon Fain
Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups. Sev…
Learning the Legibility of Visual Text Perturbations
Dev Seth, Rickard Stureborg, Danish Pruthi +1
Many adversarial attacks in NLP perturb inputs to produce visually similar strings ('ergo' 'rgo') which are legible to humans but degrade model performance. Althou…