85 citations · 118 across the 15 of their papers we have counts for
5 papers · 1 filter
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation
Arnav M. Das, Gantavya Bhatt, Lilly Kumari +2
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data…
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Sahil Verma, Royi Rassin, Arnav Das +6
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models…
Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
Hritik Bansal, Ashima Suvarna, Gantavya Bhatt +3
A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context. This method, ho…
Deep Submodular Peripteral Networks
Gantavya Bhatt, Arnav Das, Jeff Bilmes
Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering g…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…