30 citations · 95 across the 9 of their papers we have counts for
11 papers
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
Zhen Wang, Rameswar Panda, Leonid Karlinsky +3
Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting lar…
Learning to Grow Pretrained Models for Efficient Transformer Training
Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen +6
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic…
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi +2
Deployed language models decay over time due to shifting inputs, changing user needs, or emergent world-knowledge gaps. When such problems are identified, we want to make targeted…
VALHALLA: Visual Hallucination for Machine Translation
Yi Li, Rameswar Panda, Yoon Kim +4
Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising perf…
Sequence-to-Sequence Learning with Latent Neural Grammars
Yoon Kim
Sequence-to-sequence learning with neural networks has become the de facto standard for sequence prediction tasks. This approach typically models the local distribution over the ne…
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models
Matteo Alleman, Jonathan Mamou, Miguel A Del Rio +3
While vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workin…