14 citations · 22 across the 10 of their papers we have counts for
12 papers
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
Xiyao Wang, Zhengyuan Yang, Chao Feng +10
Reinforcement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that are challenging yet easily verifiable, such as math reasonin…
The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation
Yuhang Zhou, Yimin Xiao, Wei Ai +1
Social media platforms have become central to modern communication, yet they also harbor offensive content that challenges platform safety and inclusivity. While prior research has…
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data
Yuhang Zhou, Jing Zhu, Shengyi Qian +7
Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). Among RLHF methods, Group Relative Policy Op…
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
Yuhang Zhou, Giannis Karamanolakis, Victor Soto +5
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert…
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou +3
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on use…
Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios
Yuhang Zhou, Wei Ai
There is increasing interest in distilling task-specific knowledge from large language models (LLM) to smaller student models. Nonetheless, LLM distillation presents a dual challen…
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
Jing Zhu, Yuhang Zhou, Shengyi Qian +4
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in d…
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
Yuhang Zhou, Jing Zhu, Paiheng Xu +5
Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive. Knowledge distillation (KD…
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Xiyao Wang, Jiuhai Chen, Zhaoyang Wang +8
Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. Howev…
Large Language Models and Causal Inference in Collaboration: A Survey
Xiaoyu Liu, Paiheng Xu, Junda Wu +10
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal…
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
Yuhang Zhou, Xuan Lu, Wei Ai
In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolutio…
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Yuhang Zhou, Paiheng Xu, Xiyao Wang +3
Emojis, which encapsulate semantics beyond mere words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in explorin…