6 papers
CURE: Controlled Unlearning for Robust Embeddings -- Mitigating Conceptual Shortcuts in Pre-Trained Language Models
Aysenur Kocak, Shuo Yang, Bardh Prenkaj +1
Pre-trained language models have achieved remarkable success across diverse applications but remain susceptible to spurious, concept-driven correlations that impair robustness and…
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1
Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection. Contemporary approaches adopt large lan…
Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse f…
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances…
Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
Chenchen Yuan, Zheyu Zhang, Shuo Yang +2
Large Language Models (LLMs) have shown impressive moral reasoning abilities. Yet they often diverge when confronted with complex, multi-factor moral dilemmas. To address these dis…
RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting
Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
Despite the widespread use of LLMs due to their superior performance in various tasks, their high computational costs often lead potential users to opt for the pretraining-finetuni…