Papers by Scott Fujimoto
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs (2026.findings-eacl)
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Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, Yonathan Efroni
| Challenge: | Large-gradient tasks can achieve similar or even much lower learning gains than small-grading ones. |
| Approach: | They show that large-gradient tasks can achieve lower learning gains than small-grading ones . large-grade tasks can accomplish similar or even lower learning gain than small grade ones if they are large . |
| Outcome: | The proposed approach fails when certain tasks produce larger gradients . Large-gradient tasks can achieve lower learning gains than small-gradent ones . |
Sentiment Analysis: It’s Complicated! (N18-1)
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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths
| Challenge: | a dataset of over 7,000 tweets annotated with 5x coverage is used for sentiment analysis . a "complicated" class of sentiment is used to categorize text based on a predefined notion of sentiment . |
| Approach: | They propose to use a "complicated" class of sentiment to categorize tweets . they build a publicly available tweet sentiment analysis dataset . |
| Outcome: | The proposed classifiers perform better over a new publicly available TSA dataset . the classifier performance is compared with existing methods and improves on existing ones . |