๐ค AI Summary
ๆฌๆ้ๅฏนๅคงๅ่ฏญ่จๆจกๅ็้ซ็ขณ่ถณ่ฟน้ฎ้ข๏ผๆๅบ้็จ็ๅฝๅจๆๆ่ๆนๆณ๏ผ้่ฟ้ข้ฒใ้็จใๅๆถ็ญๆชๆฝๅๅฐๅ
ถ็ฏๅขๅฝฑๅใ
๐ Abstract
Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy efficiency of these models have not translated into reduced consumption due to rebound effects such as Jevons Paradox - that increased efficiency drives increased use. There is therefore a need for additional measures to solve this problem.
We suggest that one possible way forward is to use life cycle thinking, and view LLMs as products that can become waste. With this perspective, we investigate the potential of the waste hierarchy from the EU's Waste Framework Directive, which suggests five different measures for how to manage waste: prevention, reuse, recycling, recovery, and disposal. We examine how these measures can inform and motivate new types of thinking and approaches to reducing LLM waste and their environmental impact in general.
Applying the waste hierarchy to LLMs highlights that preventing waste is essential for reducing the models' environmental impact, mainly because it reduces the need for training new models. Prevention can be achieved through many existing methods for reusing, "recycling", and "recovering" LLMs. Additionally, disposal can be important both for saving energy and for keeping a considerate attitude to the resources being spent on training LLMs. We also call to attention that prevention of unnecessary use of LLMs carry huge potential for lowering the climate impact of the models.