565 citations · 610 across the 78 of their papers we have counts for
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MEAL: Stable and Active Learning for Few-Shot Prompting
Abdullatif Köksal, Timo Schick, Hinrich Schütze
Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners. However, this approach has high…
Federated Continual Learning for Text Classification via Selective Inter-client Transfer
Yatin Chaudhary, Pranav Rai, Matthias Schubert +2
In this work, we combine the two paradigms: Federated Learning (FL) and Continual Learning (CL) for text classification task in cloud-edge continuum. The objective of Federated Con…
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Yanai Elazar, Nora Kassner, Shauli Ravfogel +6
Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?
Marina Sedinkina, Martin Schmitt, Hinrich Schütze
The practical success of much of NLP depends on the availability of training data. However, in real-world scenarios, training data is often scarce, not least because many applicati…