Papers by Victor Sanh
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)
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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush
| Challenge: | PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings . |
| Approach: | PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development . |
| Outcome: | PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines . |
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning (2021.emnlp-main)
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| Challenge: | Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream task instances as a language modeling problem. |
| Approach: | They propose to reformulate downstream tasks as a language modeling problem and add a regularization that preserves pretraining weights to the model to mitigate the destructive tendency of few-shot finetuning. |
| Outcome: | The proposed model performs better on low data regimes than the standard model on few-shot finetuning. |
What Language Model to Train if You Have One Million GPU Hours? (2022.findings-emnlp)
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Teven Le Scao, Thomas Wang, Daniel Hesslow, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy
| Challenge: | Recent years have seen the advent of large language models characterized by emergent capabilities arising from sheer scale alone. |
| Approach: | They propose to use a multilingual model to compare performance to the English-only model by ablation at the billion-parameter scale. |
| Outcome: | The proposed model is based on a multilingual model and its performance against the English-only model. |
Datasets: A Community Library for Natural Language Processing (2021.emnlp-demo)
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Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, Thomas Wolf
| Challenge: | Contemporary NLP systems use many different datasets at significantly varying scale and level of annotation. |
| Approach: | a community library for contemporary NLP is available at https://github.com/datasets . the library includes more than 650 unique datasets and has more than 250 contributors a year after its initial development . |
| Outcome: | the library includes more than 650 unique datasets and has more than 250 contributors . it supports a variety of cross-dataset research projects and shared tasks . |
Low-Complexity Probing via Finding Subnetworks (2021.naacl-main)
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| Challenge: | Existing approaches to probing neural networks for linguistic properties are to train a shallow multi-layer perceptron (MLP) on top of the model's internal representations. |
| Approach: | They propose a subtractive pruning-based probe where they find an existing subnetwork that performs the linguistic task of interest. |
| Outcome: | The proposed probe achieves higher accuracy on pre-trained models and lower accuracy on random models, and better learning on its own. |
Block Pruning For Faster Transformers (2021.emnlp-main)
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| Challenge: | Pruning methods have proven to be effective at reducing model size, while distillation methods are proven for speeding up inference. |
| Approach: | They propose a block pruning approach that integrates structured pruning methods with the movement pruning paradigm for fine-tuning. |
| Outcome: | The proposed model is 2.4x faster, 74% smaller and faster than distilled models on classification and generation tasks. |
Transformers: State-of-the-Art Natural Language Processing (2020.emnlp-demos)
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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
| Challenge: | Transformers is an open-source library that aims to open up advances in natural language processing to the wider machine learning community. |
| Approach: | they propose an open-source library that aims to open up advances in machine learning to the wider community. |
| Outcome: | Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. |