Papers by Samuel Thomas
Crosslingual Generalization through Multitask Finetuning (2023.acl-long)
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Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
| Challenge: | 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. |
| Approach: | They apply multitask prompted finetuning to pretrained multilingual models and generate variants called BLOOMZ and mT0. |
| Outcome: | The proposed models can generalize to non-English languages that have never been seen before. |
A Recorded Debating Dataset (L18-1)
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Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
FinGPT: Large Generative Models for a Small Language (2023.emnlp-main)
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Risto Luukkonen, Ville Komulainen, Jouni Luoma, Anni Eskelinen, Jenna Kanerva, Hanna-Mari Kupari, Filip Ginter, Veronika Laippala, Niklas Muennighoff, Aleksandra Piktus, Thomas Wang, Nouamane Tazi, Teven Scao, Thomas Wolf, Osma Suominen, Samuli Sairanen, Mikko Merioksa, Jyrki Heinonen, Aija Vahtola, Samuel Antao, Sampo Pyysalo
| Challenge: | Neural language models excel in many tasks in NLP but are limited to smaller languages. |
| Approach: | They propose two approaches to pretrain large language models for Finnish . they train seven monolingual models from scratch and use Finnish as pretraining data . |
| Outcome: | The proposed model is based on a dataset of Finnish web crawls, news, social media and eBooks. |