195 citations · 575 across the 15 of their papers we have counts for
5 papers · 1 filter
Is margin all you need? An extensive empirical study of active learning on tabular data
Dara Bahri, Heinrich Jiang, Tal Schuster +1
Given a labeled training set and a collection of unlabeled data, the goal of active learning (AL) is to identify the best unlabeled points to label. In this comprehensive study, we…
Confident Adaptive Language Modeling
Tal Schuster, Adam Fisch, Jai Gupta +5
Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks. These gains come with a drastic increase in th…
UL2: Unifying Language Learning Paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran +11
Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-traini…
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
Kai Hui, Honglei Zhuang, Tao Chen +8
State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradig…
Transformer Memory as a Differentiable Search Index
Yi Tay, Vinh Q. Tran, Mostafa Dehghani +10
In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of th…