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On the Role of Parallel Data in Cross-lingual Transfer Learning
Machel Reid, Mikel Artetxe
While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the…
DiffusER: Discrete Diffusion via Edit-based Reconstruction
Machel Reid, Vincent J. Hellendoorn, Graham Neubig
In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revis…
M2D2: A Massively Multi-domain Language Modeling Dataset
Machel Reid, Victor Zhong, Suchin Gururangan +1
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted…
Learning to Model Editing Processes
Machel Reid, Graham Neubig
Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating…
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid +2
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specifi…
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan +42
Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these dat…
Can Wikipedia Help Offline Reinforcement Learning?
Machel Reid, Yutaro Yamada, Shixiang Shane Gu
Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among differ…