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Transparency and governance Methodologists Directly upstream of ai100's methodology

Emma Strubell

Whether the compute and energy cost of training and serving language models belongs in the headline of an evaluation, where accuracy currently sits alone.

Strubell's 2019 paper "Energy and Policy Considerations for Deep Learning in NLP" put numbers to a thing the field had been quietly ignoring: training a single large language model could produce CO2 emissions comparable to several cars over their lifetimes, and the gap between research-paper accuracy reports and the carbon cost of producing them was widening with every new state-of-the-art. The work reframed efficiency from a nice-to-have engineering concern into an evaluation dimension that should show up next to accuracy in every model comparison. For ai100, which evaluates models that vary by orders of magnitude in inference cost, this is the methodological backing for treating "good answer" and "good answer at what cost" as different questions.

Worth following when
you want to report or audit an LLM result with compute and energy cost treated as first-class numbers.
Topics
energy and compute cost of language-model training and serving; equitable NLP and access-to-compute as research problems; efficient model design as a methodology in its own right.
Key works
"Energy and Policy Considerations for Deep Learning in NLP" (2019, lead author); Green AI position paper (2019, co-author); ongoing CMU work on efficient and equitable NLP.