Current generative transformer-based models have achieved state-of-the-art performance in long document summarization. However, this task witnessed a paradigm shift in developing ever-increasingly computationally-hungry models, focusing on effectiveness while ignoring the economic, environmental, and social costs of producing such results. Consequently, such extensive resources impact climate change and raise barriers to small and medium organizations characterized by low-resource regimes of hardware and data. As a result, this unsustainable trend has lifted many concerns in the community, proposing tools to monitor models' energy costs. Despite their importance, no evaluation measure considering models' eco-sustainability exists yet. In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. We perform an extensive benchmark for long document summarization, comparing multiple state-of-the-art quadratic and linear transformers on several datasets under eco-sustainable regimes. Finally, thanks to Carburacy, we found optimal combinations of hyperparameters that let models be competitive in effectiveness with significantly lower costs.

Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy / Gianluca Moro, Luca Ragazzi, Lorenzo Valgimigli. - ELETTRONICO. - (2023), pp. 14417-14425. (Intervento presentato al convegno AAAI tenutosi a Washington D.C. nel February) [10.1609/aaai.v37i12.26686].

Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy

Gianluca Moro;Luca Ragazzi
;
Lorenzo Valgimigli
2023

Abstract

Current generative transformer-based models have achieved state-of-the-art performance in long document summarization. However, this task witnessed a paradigm shift in developing ever-increasingly computationally-hungry models, focusing on effectiveness while ignoring the economic, environmental, and social costs of producing such results. Consequently, such extensive resources impact climate change and raise barriers to small and medium organizations characterized by low-resource regimes of hardware and data. As a result, this unsustainable trend has lifted many concerns in the community, proposing tools to monitor models' energy costs. Despite their importance, no evaluation measure considering models' eco-sustainability exists yet. In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. We perform an extensive benchmark for long document summarization, comparing multiple state-of-the-art quadratic and linear transformers on several datasets under eco-sustainable regimes. Finally, thanks to Carburacy, we found optimal combinations of hyperparameters that let models be competitive in effectiveness with significantly lower costs.
2023
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence
14417
14425
Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy / Gianluca Moro, Luca Ragazzi, Lorenzo Valgimigli. - ELETTRONICO. - (2023), pp. 14417-14425. (Intervento presentato al convegno AAAI tenutosi a Washington D.C. nel February) [10.1609/aaai.v37i12.26686].
Gianluca Moro, Luca Ragazzi, Lorenzo Valgimigli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/913146
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