59 citations · 240 across the 38 of their papers we have counts for
11 papers · 1 filter
Leveraging Graph Diffusion Models for Network Refinement Tasks
Puja Trivedi, Ryan Rossi, David Arbour +7
Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically imp…
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi +3
There is growing interest in systems that generate captions for scientific figures. However, assessing these systems output poses a significant challenge. Human evaluation requires…
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Yuchen Zhuang, Xiang Chen, Tong Yu +5
Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can intera…
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo +4
A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressiv…
Knowledge Graph Prompting for Multi-Document Question Answering
Yu Wang, Nedim Lipka, Ryan A. Rossi +3
The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this…
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Ashish Singh, Ashutosh Singh, Prateek Agarwal +10
Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from docum…
Learning the Visualness of Text Using Large Vision-Language Models
Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer +2
Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and ge…
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim +4
We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer…
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi +6
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation…