78 citations · 230 across the 36 of their papers we have counts for
4 papers · 1 filter
Fact-Level Confidence Calibration and Self-Correction
Yige Yuan, Bingbing Xu, Hexiang Tan +5
Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their o…
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Yige Yuan, Bingbing Xu, Teng Xiao +4
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at ba…
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Zixu Wang, Bingbing Xu, Yige Yuan +2
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-dist…
Graph Domain Adaptation: Challenges, Progress and Prospects
Boshen Shi, Yongqing Wang, Fangda Guo +3
As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective know…