8 citations · 11 across the 4 of their papers we have counts for
4 papers
Revisiting Generalization Across Difficulty Levels: It's Not So Easy
Yeganeh Kordi, Nihal V. Nayak, Max Zuo +2
We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is…
K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
Tassallah Abdullahi, Ioanna Gemou, Nihal V. Nayak +4
Biomedical knowledge graphs (KGs) encode rich, structured information critical for drug discovery tasks, but extracting meaningful insights from large-scale KGs remains challenging…
$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources
Apoorv Khandelwal, Tian Yun, Nihal V. Nayak +4
Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In…
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Nihal V. Nayak, Yiyang Nan, Avi Trost +1
We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. We aim to enab…