12 citations · 40 across the 20 of their papers we have counts for
4 papers · 1 filter
Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention
Zhen Tan, Tianlong Chen, Zhenyu Zhang +1
Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. However, the enigmatic ``black-box'' nature of LLMs remains a…
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Yifan Li, Zhen Tan, Kai Shu +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when t…
Interpreting Pretrained Language Models via Concept Bottlenecks
Zhen Tan, Lu Cheng, Song Wang +3
Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. However, the lack of interpretability due to their ``black-box'' natur…
Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance
Song Wang, Zhen Tan, Ruocheng Guo +1
Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language proc…