most citedHow Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts

4 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

cs.LG2025

The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound

Clément Morand, Anne-Laure Ligozat, Aurélie Névéol

Recent Machine Learning (ML) approaches have shown increased performance on benchmarks at the cost of escalating compute demands. Hardware, algorithmic and carbon optimizations hav…

cs.LG2024★ 4 cited

How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts

Clément Morand, Anne-Laure Ligozat, Aurélie Névéol

The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consu…

cs.CL2024

New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark

Nadège Alavoine, Gaëlle Laperriere, Christophe Servan +2

Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fi…

cs.CL2024

A Benchmark Evaluation of Clinical Named Entity Recognition in French

Nesrine Bannour, Christophe Servan, Aurélie Névéol +1

Background: Transformer-based language models have shown strong performance on many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest…

cs.AI2024

mALBERT: Is a Compact Multilingual BERT Model Still Worth It?

Christophe Servan, Sahar Ghannay, Sophie Rosset

Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models. In this article, considering the…