Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models. - Clara Na; Sanket Vaibhav Mehta; Emma Strubell (EMNLP (Findings), 2022)

The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment. - Jared Fernandez; Jacob Kahn; Clara Na; Yonatan Bisk; Emma Strubell (EMNLP, 2023)

To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing. - Sireesh Gururaja; Amanda Bertsch; Clara Na; David Gray Widder; Emma Strubell (EMNLP, 2023)

Energy and Carbon Considerations of Fine-Tuning BERT. - Xiaorong Wang; Clara Na; Emma Strubell; Sorelle A. Friedler; Sasha Luccioni (EMNLP (Findings), 2023)

The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment. - Jared Fernandez; Jacob Kahn; Clara Na; Yonatan Bisk; Emma Strubell (CoRR, 2023)

Scalable Data Ablation Approximations for Language Models through Modular Training and Merging. - Clara Na; Ian Magnusson; Ananya Harsh Jha; Tom Sherborne; Emma Strubell; Jesse Dodge; Pradeep Dasigi (EMNLP, 2024)

Energy Considerations of Large Language Model Inference and Efficiency Optimizations. - Jared Fernandez; Clara Na; Vashisth Tiwari; Yonatan Bisk; Sasha Luccioni; Emma Strubell (ACL (1), 2025)

Holistically Evaluating the Environmental Impact of Creating Language Models. - Jacob Morrison; Clara Na; Jared Fernandez; Tim Dettmers; Emma Strubell; Jesse Dodge (ICLR, 2025)