12 citations · 23 across the 6 of their papers we have counts for
17 papers · 1 filter
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…
Extracting Latent Steering Vectors from Pretrained Language Models
Nishant Subramani, Nivedita Suresh, Matthew E. Peters
Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired…
Staged Training for Transformer Language Models
Sheng Shen, Pete Walsh, Kurt Keutzer +3
The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training…
CDLM: Cross-Document Language Modeling
Avi Caciularu, Arman Cohan, Iz Beltagy +3
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First,…
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Alexis Ross, Ana Marasović, Matthew E. Peters
Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case). Despite the i…
Learning from Task Descriptions
Orion Weller, Nicholas Lourie, Matt Gardner +1
Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an exam…