5 citations · 7 across the 17 of their papers we have counts for
7 papers · 1 filter
When Data Falls Short: Grokking Below the Critical Threshold
Vaibhav Singh, Eugene Belilovsky, Rahaf Aljundi
In this paper, we investigate the phenomenon of grokking, where models exhibit delayed generalization following overfitting on training data. We focus on data-scarce regimes where…
DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
Vaibhav Singh, Oleksiy Ostapenko, Pierre-André Noël +2
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) generation, yet their reliance on Transformer backbones limits inference efficiency…
KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning
Vaibhav Singh, Soumya Suvra Ghosal, Kapu Nirmal Joshua +2
In-context learning (ICL) has emerged as a powerful paradigm for adapting large language models (LLMs) to new and data-scarce tasks using only a few carefully selected task-specifi…
Model Parallelism With Subnetwork Data Parallelism
Vaibhav Singh, Zafir Khalid, Pietro Cagnasso +2
Pre-training large neural networks at scale imposes heavy memory demands on accelerators and often requires costly communication. We introduce Subnetwork Data Parallelism (SDP), a…
Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples
Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh +4
Reward models are essential for aligning large language models (LLMs) with human preferences. However, most open-source multilingual reward models are primarily trained on preferen…
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Vaibhav Singh, Paul Janson, Paria Mehrbod +4
The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has em…
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification
Yashwanth M., Vaibhav Singh, Ayush Maheshwari +2
We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction…