Papers by Lintang Sutawika
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions (2025.emnlp-main)
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Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig
| Challenge: | Language model performance is largely dependent on pretraining decisions, but scaling laws based on only these two aspects do not always explain downstream task performance. |
| Approach: | They meta-analyze 92 open-source pretrained models to quantify their impact on performance. |
| Outcome: | The framework lays a foundation for more systematic investigation of how model development choices shape final capabilities. |
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. |
Re-Evaluating Evaluation for Multilingual Summarization (2024.emnlp-main)
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Jessica Forde, Ruochen Zhang, Lintang Sutawika, Alham Aji, Samuel Cahyawijaya, Genta Winata, Minghao Wu, Carsten Eickhoff, Stella Biderman, Ellie Pavlick
| Challenge: | Existing studies have shown that automated evaluation approaches correlate with human ratings in English, but this is unclear for other languages. |
| Approach: | They construct a small-scale pilot dataset containing article-summary pairs and human ratings in English, Chinese and Indonesian to measure the strength of summaries. |
| Outcome: | The results show that standard metrics are unreliable measures of quality in Chinese and Indonesian. |
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. |
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting (2023.acl-long)
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Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, Alham Fikri Aji, David Ifeoluwa Adelani, Khalid Almubarak, M Saiful Bari, Lintang Sutawika, Jungo Kasai, Ahmed Baruwa, Genta Winata, Stella Biderman, Edward Raff, Dragomir Radev, Vassilina Nikoulina
| Challenge: | Existing language adaptation strategies for multilingual models are limited to 46 languages . a new language is added to the model to improve zero-shot prompting performance . |
| Approach: | They apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. |
| Outcome: | The proposed model can be extended to other languages without incurring prohibitively large costs. |
Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning (2026.acl-long)
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| Challenge: | Current reasoning large language models (RLMs) are trained on data that is primarily in English, resulting in lower performance when asked the same question in a non-English language. |
| Approach: | They propose a framework for enhancing multilingual reasoning without any data in the target language(s). |
| Outcome: | The proposed framework outperforms Qwen2.5-7B-Instruct on 4 math and non-math tasks with less than 1/8 of the training data (125). |