Sam Shleifer


2022

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Few-shot Learning with Multilingual Generative Language Models
Xi Victoria Lin | Todor Mihaylov | Mikel Artetxe | Tianlu Wang | Shuohui Chen | Daniel Simig | Myle Ott | Naman Goyal | Shruti Bhosale | Jingfei Du | Ramakanth Pasunuru | Sam Shleifer | Punit Singh Koura | Vishrav Chaudhary | Brian O’Horo | Jeff Wang | Luke Zettlemoyer | Zornitsa Kozareva | Mona Diab | Veselin Stoyanov | Xian Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual generative language models on a corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We conduct an in-depth analysis of different multilingual prompting approaches, showing in particular that strong few-shot learning performance across languages can be achieved via cross-lingual transfer through both templates and demonstration examples.

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Efficient Large Scale Language Modeling with Mixtures of Experts
Mikel Artetxe | Shruti Bhosale | Naman Goyal | Todor Mihaylov | Myle Ott | Sam Shleifer | Xi Victoria Lin | Jingfei Du | Srinivasan Iyer | Ramakanth Pasunuru | Giridharan Anantharaman | Xian Li | Shuohui Chen | Halil Akin | Mandeep Baines | Louis Martin | Xing Zhou | Punit Singh Koura | Brian O’Horo | Jeffrey Wang | Luke Zettlemoyer | Mona Diab | Zornitsa Kozareva | Veselin Stoyanov
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning. With the exception of fine-tuning, we find MoEs to be substantially more compute efficient. At more modest training budgets, MoEs can match the performance of dense models using ~4 times less compute. This gap narrows at scale, but our largest MoE model (1.1T parameters) consistently outperforms a compute-equivalent dense model (6.7B parameters). Overall, this performance gap varies greatly across tasks and domains, suggesting that MoE and dense models generalize differently in ways that are worthy of future study. We make our code and models publicly available for research use.

2020

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Transformers: State-of-the-Art Natural Language Processing
Thomas Wolf | Lysandre Debut | Victor Sanh | Julien Chaumond | Clement Delangue | Anthony Moi | Pierric Cistac | Tim Rault | Remi Louf | Morgan Funtowicz | Joe Davison | Sam Shleifer | Patrick von Platen | Clara Ma | Yacine Jernite | Julien Plu | Canwen Xu | Teven Le Scao | Sylvain Gugger | Mariama Drame | Quentin Lhoest | Alexander Rush
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.