4 papers
CATTO: Balancing Preferences and Confidence in Language Models
Nisarg Parikh, Ananya Sai, Pannaga Shivaswamy +2
Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequent…
LookAlike: Consistent Distractor Generation in Math MCQs
Nisarg Parikh, Nigel Fernandez, Alexander Scarlatos +2
Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approa…
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
Kunjal Panchal, Nisarg Parikh, Sunav Choudhary +3
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using pr…
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Kunjal Panchal, Sunav Choudhary, Nisarg Parikh +2
Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coar…