29 citations · 92 across the 18 of their papers we have counts for
9 papers · 1 filter
Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models
Yuqing Wang, Yun Zhao, Sara Alessandra Keller +4
The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have f…
Language and Multimodal Models in Sports: A Survey of Datasets and Applications
Haotian Xia, Zhengbang Yang, Yun Zhao +7
Recent integration of Natural Language Processing (NLP) and multimodal models has advanced the field of sports analytics. This survey presents a comprehensive review of the dataset…
RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models
Yuqing Wang, Yun Zhao
With the increasing use of large language models (LLMs), ensuring reliable performance in diverse, real-world environments is essential. Despite their remarkable achievements, LLMs…
SportQA: A Benchmark for Sports Understanding in Large Language Models
Haotian Xia, Zhengbang Yang, Yuqing Wang +7
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the…
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
Yuqing Wang, Yun Zhao
The burgeoning interest in Multimodal Large Language Models (MLLMs), such as OpenAI's GPT-4V(ision), has significantly impacted both academic and industrial realms. These models en…
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
Yuqing Wang, Prashanth Vijayaraghavan, Ehsan Degan
Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors a…
TRAM: Benchmarking Temporal Reasoning for Large Language Models
Yuqing Wang, Yun Zhao
Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by…
Metacognitive Prompting Improves Understanding in Large Language Models
Yuqing Wang, Yun Zhao
In Large Language Models (LLMs), there have been consistent advancements in task-specific performance, largely influenced by effective prompt design. Recent advancements in prompti…
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
Yuqing Wang, Yun Zhao, Linda Petzold
Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks prese…