16 citations · 39 across the 14 of their papers we have counts for
15 papers
Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation
Nishant Balepur, Paiheng Xu, Wei Ai +3
Multiple-choice question answering (MCQA) benchmarks in NLP use number-right scoring (accuracy), but in educational testing, the scoring scheme, the combination of the response mod…
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…
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs
Paiheng Xu, Gang Wu, Xiang Chen +6
Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specifi…
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…
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…
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…
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
Yuhang Zhou, Paiheng Xu, Xiaoyu Liu +3
Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate excep…
Emoji Promotes Developer Participation and Issue Resolution on GitHub
Yuhang Zhou, Xuan Lu, Ge Gao +2
Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-ver…
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis +4
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including th…
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Paiheng Xu, Yuhang Zhou, Bang An +2
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs ha…