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07/19/2023
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Rising computational demands of modern natural language processing (NLP)...
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05/24/2023
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
The integration of multi-document pre-training objectives into language ...
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05/15/2023
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obt...
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02/14/2023
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models
Pretrained language models (PLMs) are trained on massive corpora, but of...
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10/24/2022
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Rationalization is fundamental to human reasoning and learning. NLP mode...
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05/24/2022
Attentional Mixtures of Soft Prompt Tuning for Parameter-efficient Multi-task Knowledge Sharing
This work introduces ATTEMPT (Attentional Mixture of Prompt Tuning), a n...
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05/10/2022
Extracting Latent Steering Vectors from Pretrained Language Models
Prior work on controllable text generation has focused on learning how t...
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03/15/2022
Hyperdecoders: Instance-specific decoders for multi-task NLP
We investigate input-conditioned hypernetworks for multi-tasking in NLP,...
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12/16/2021
Efficient Hierarchical Domain Adaptation for Pretrained Language Models
Generative language models are trained on diverse, general domain corpor...
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11/16/2021
Few-Shot Self-Rationalization with Natural Language Prompts
Self-rationalization models that predict task labels and generate free-t...
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07/15/2021
Tailor: Generating and Perturbing Text with Semantic Controls
Making controlled perturbations is essential for various tasks (e.g., da...
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04/17/2021
Competency Problems: On Finding and Removing Artifacts in Language Data
Much recent work in NLP has documented dataset artifacts, bias, and spur...
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01/02/2021
Cross-Document Language Modeling
We introduce a new pretraining approach for language models that are gea...
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12/27/2020
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Humans give contrastive explanations that explain why an observed event ...
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11/16/2020
Learning from Task Descriptions
Typically, machine learning systems solve new tasks by training on thous...
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04/10/2020
Longformer: The Long-Document Transformer
Transformer-based models are unable to process long sequences due to the...
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02/10/2020
Adversarial Filters of Dataset Biases
Large neural models have demonstrated human-level performance on languag...
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09/09/2019
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unla...
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08/27/2018
Dissecting Contextual Word Embeddings: Architecture and Representation
Contextual word representations derived from pre-trained bidirectional l...
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02/15/2018
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that ...
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04/29/2017
