Papers by Rickard Stureborg
Tailoring Vaccine Messaging with Common-Ground Opinions (2024.findings-naacl)
Copied to clipboard
Rickard Stureborg, Sanxing Chen, Roy Xie, Aayushi Patel, Christopher Li, Chloe Zhu, Tingnan Hu, Jun Yang, Bhuwan Dhingra
| Challenge: | Vaccine interventions aim to answer concerns expressed about vaccination. |
| Approach: | They propose a dataset to evaluate how well responses are tailored to a common-ground opinion . they find that GPT-4-Turbo performs significantly better than others . |
| Outcome: | The proposed dataset outperforms fine tuned LLMs on the task of tailoring vaccine responses to common-ground opinions. |
Characterizing the Confidence of Large Language Model-Based Automatic Evaluation Metrics (2024.eacl-short)
Copied to clipboard
| Challenge: | Recent studies have focused on using Large Language Models (LLMs) to evaluate NLP tasks automatically. |
| Approach: | They characterize LLM evaluators’ confidence in ranking candidate NLP models and develop a configurable Monte Carlo simulation method to compensate for loss of correlation. |
| Outcome: | The proposed method can reach 95% confidence rankings of candidate models with reasonable evaluation set sizes. |
MatViX: Multimodal Information Extraction from Visually Rich Articles (2025.naacl-long)
Copied to clipboard
Ghazal Khalighinejad, Sharon Scott, Ollie Liu, Kelly L. Anderson, Rickard Stureborg, Aman Tyagi, Bhuwan Dhingra
| Challenge: | Existing methods for multimodal information extraction are limited due to the multimodal nature of scientific articles and complex interconnections between data points. |
| Approach: | They propose a benchmark to extract structured information from scientific articles . they use curated JSON files extracted from text, tables, and figures . |
| Outcome: | The proposed benchmark is based on 324 full-length research articles and 1,688 complex structured JSON files curated by experts in polymer nanocomposites and biodegradation. |
Learning the Legibility of Visual Text Perturbations (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing adversarial attacks in NLP perturb text to produce visually similar strings ('ergo', 'rgo') which are legible to humans but degrade model performance. |
| Approach: | They use a human-annotated dataset comprising the legibility of visually perturbed text to build models that predict the legible inputs and rank them based on their legibility. |
| Outcome: | The proposed models achieve an F score of 0.91 and an accuracy of 0.86 in predicting which of two perturbations is more legible. |