4 papers
Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
Jiali Cheng, Chirag Agarwal, Hadi Amiri
Knowledge distillation (KD) is an effective method for model compression and transferring knowledge between models. However, its effect on model's robustness against spurious corre…
Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces
Runtao Zhou, Giang Nguyen, Nikita Kharya +2
The reasoning capabilities of Large Language Models (LLMs) have led to their increasing employment in several critical applications, particularly education, where they support prob…
A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models
Soham Petkar, Hari Aakash K, Anirudh Vempati +3
Developments in Graph-Language Models (GLMs) aim to integrate the structural reasoning capabilities of Graph Neural Networks (GNNs) with the semantic understanding of Large Languag…
Rethinking Explainability in the Era of Multimodal AI
Chirag Agarwal
While multimodal AI systems (models jointly trained on heterogeneous data types such as text, time series, graphs, and images) have become ubiquitous and achieved remarkable perfor…