activity
20202024
most citedmT5: A massively multilingual pre-trained text-to-text transformer

602 citations · 920 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024★ 238 cited

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Thomas Mesnard, Cassidy Hardin +105

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…

cs.CL2024★ 1 cited

Transfer Learning for Text Diffusion Models

Kehang Han, Kathleen Kenealy, Aditya Barua +2

In this report, we explore the potential for text diffusion to replace autoregressive (AR) decoding for the training and deployment of large language models (LLMs). We are particul…

cs.CL2022★ 1 cited

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

Tu Vu, Aditya Barua, Brian Lester +3

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case…

cs.CL2021★ 69 cited

ByT5: Towards a token-free future with pre-trained byte-to-byte models

Linting Xue, Aditya Barua, Noah Constant +5

Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw te…

cs.CL2020★ 602 cited

mT5: A massively multilingual pre-trained text-to-text transformer

Linting Xue, Noah Constant, Adam Roberts +5

The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP t…

cs.CL2020★ 9 cited

LAReQA: Language-agnostic answer retrieval from a multilingual pool

Uma Roy, Noah Constant, Rami Al-Rfou +3

We present LAReQA, a challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool. Unlike previous cross-lingual tasks, LAReQA tests for "str…