3 citations · 7 across the 10 of their papers we have counts for
10 papers
Distribution Corrected Offline Data Distillation for Large Language Models
Yumeng Zhang, Zhengbang Yang, Yevin Nikhel Goonatilake +1
Distilling reasoning traces from strong large language models into smaller ones is a promising route to improve intelligence in resource-constrained settings. Existing approaches f…
Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification
Zhengbang Yang, Md. Tasin Tazwar, Minghan Wei +1
Robotic path planning problems are often NP-hard, and practical solutions typically rely on approximation algorithms with provable performance guarantees for general cases. While d…
DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher
Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu
LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which is indispensable towards trustworthy AI. Existing…
CALIBURN: Self-Calibrated LLM Unlearning Alignment
Zhengbang Yang, Yisheng Zhong, Junyuan Hong +1
LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for addressing safety and privacy concerns.…
Hierarchical Federated Unlearning for Large Language Models
Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu
Large Language Models (LLMs) are increasingly integrated into real-world applications, raising concerns about privacy, security and the need to remove undesirable knowledge. Machin…
ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults
Zhengbang Yang, Junyuan Hong, Yijiang Pang +2
Cognitive health in older adults presents a growing challenge. Although conversational interventions show feasibility in improving cognitive wellness, human caregiver resources rem…
SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models
Haotian Xia, Zhengbang Yang, Junbo Zou +10
Multimodal Large Language Models (MLLMs) are advancing the ability to reason about complex sports scenarios by integrating textual and visual information. To comprehensively evalua…
Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video
Zhengbang Yang, Haotian Xia, Jingxi Li +3
Understanding sports is crucial for the advancement of Natural Language Processing (NLP) due to its intricate and dynamic nature. Reasoning over complex sports scenarios has posed…
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…
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…