Vadim Dabravolski
2024
Academics Can Contribute to Domain-Specialized Language Models
Mark Dredze
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Genta Winata
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Prabhanjan Kambadur
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Shijie Wu
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Ozan Irsoy
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Steven Lu
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Vadim Dabravolski
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David Rosenberg
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Sebastian Gehrmann
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Commercially available models dominate academic leaderboards. While impressive, this has concentrated research on creating and adapting general-purpose models to improve NLP leaderboard standings for large language models. However, leaderboards collect many individual tasks and general-purpose models often underperform in specialized domains; domain-specific or adapted models yield superior results. This focus on large general-purpose models excludes many academics and draws attention away from areas where they can make important contributions. We advocate for a renewed focus on developing and evaluating domain- and task-specific models, and highlight the unique role of academics in this endeavor.
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Co-authors
- Mark Dredze 1
- Genta Indra Winata 1
- Prabhanjan Kambadur 1
- Shijie Wu 1
- Ozan İrsoy 1
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