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Thomas Wang

7 accepted papers

2023

Crosslingual Generalization through Multitask Finetuning

ACL 2023long

Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned varia…

2023

FinGPT: Large Generative Models for a Small Language

EMNLP 2023long main

Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finni…

Cited by 0SourceScholar
2023

OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents

NeurIPS 2023poster

Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. W…

2022

Multitask Prompted Training Enables Zero-Shot Task Generalization

ICLR 2022spotlight

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot genera…

2022

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

NeurIPS 2022accept

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large lan…

Cited by 214SourcePDFScholar
2022

What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?

ICML 2022spotlight

Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ significantly, an…

2022

What Language Model to Train if You Have One Million GPU Hours?

EMNLP 2022finding

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across tasks and scale, increasing the impact of modeling research. However, with the emergence of state-of-the-art 100B+ parame…