5 citations · 5 across the 1 of their papers we have counts for
6 papers
LLM-based Human Simulations Have Not Yet Been Reliable
Qian Wang, Jiaying Wu, Zichen Jiang +6
Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current LLM-based human simulations rema…
Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning
Qian Wang, Xuandong Zhao, Zirui Zhang +4
Large language models (LLMs) increasingly serve as reasoners and automated evaluators, yet they remain susceptible to cognitive biases -- often altering their reasoning when faced…
Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Zhen Zhang, Bingsheng He
Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domai…
PyGDA: A Python Library for Graph Domain Adaptation
Zhen Zhang, Meihan Liu, Bingsheng He
Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their…
Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation
Zhen Zhang, Bingsheng He
Unsupervised graph domain adaptation (UGDA) focuses on transferring knowledge from labeled source graph to unlabeled target graph under domain discrepancies. Most existing UGDA met…
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
Meihan Liu, Zhen Zhang, Jiachen Tang +3
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the p…