2.2k citations · 2.5k across the 23 of their papers we have counts for
31 papers
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Hao Peng, Qingqing Cao, Jesse Dodge +11
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental co…
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
Avi Caciularu, Matthew E. Peters, Jacob Goldberger +2
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose…
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae +4
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models to na…
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models
Alexandra Chronopoulou, Matthew E. Peters, Alexander Fraser +1
Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an ada…
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation
Hamish Ivison, Akshita Bhagia, Yizhong Wang +2
Recent NLP models have shown the remarkable ability to effectively generalise `zero-shot' to new tasks using only natural language instructions as guidance. However, many of these…
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Alexis Ross, Matthew E. Peters, Ana Marasović
Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been inves…