Imran Razzak

Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings

2024 · arXiv (Cornell University)

Large language models (LLMs) have garnered significant interest in natural language processing (NLP), particularly their remarkable performance in various downstream tasks in resource-rich languages. Recent studies have highlighted the limitations of LLMs in low-resource languages, …

HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network

2025 · arXiv (Cornell University)

Graph Transformers have recently achieved remarkable progress in graph representation learning by capturing long-range dependencies through self-attention. However, their quadratic computational complexity and inability to effectively model heterogeneous semantics severely limit their scalability and generalization …