Scientific Paper · source-verified

Energy and Policy Considerations for Deep Learning in NLP

Strubell, Emma; Ganesh, Ananya; McCallum, Andrew · 2019-06-05

Training large neural networks improves NLP accuracy but requires significant computational resources, leading to high financial and environmental costs.

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Strubell, Emma; Ganesh, Ananya; McCallum, Andrew. 2019-06-05. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/abs/1906.02243 (AI & Environment Resource Hub; record paper-002; collection snapshot 2026-09-15).

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Available source metadata

Author or creator
Strubell, Emma; Ganesh, Ananya; McCallum, Andrew
Publisher
arXiv
Publication date
2019-06-05
Date precision
day
Geographic scope
Unknown
Language
unknown
Peer review
unknown
Source status
verified
Last verified
2026-09-15T16:44:19.911Z
Snapshot import
2026-09-06
Legacy domain
Energy
Legacy subdomain
AI Energy Consumption in NLP
journal
arXiv

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Source sheet: Scientific Paper · Row 6 · Original ID: paper-002.

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A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.

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