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arXiv Machine Learning By Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard

Wasted large language models: A life cycle thinking approach

Read the original on arXiv Machine Learning →

The paper argues that large language models (LLMs) have a growing carbon footprint and that current efficiency gains are offset by rebound effects such as Jevons Paradox. It proposes applying the EU waste hierarchy—prevention, reuse, recycling, recovery, and disposal—to LLMs, suggesting that preventing waste and unnecessary use can significantly reduce environmental impact. The authors emphasize that treating LLMs as products that can become waste offers new ways to motivate more sustainable practices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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