被浪费的大语言模型：一种生命周期思维方法
Wasted large language models: A life cycle thinking approach
- SINTEF Digital（SINTEF数字研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将生命周期思维与欧盟废弃物层级应用于LLMs，提出通过预防、再利用等措施减少LLMs废弃物及环境影响，强调预防不必要使用的重要性。
AI中文摘要：
大语言模型（Large Language Models, LLMs）是机器学习（Machine Learning, ML）模型，其开发与使用过程中的碳足迹正日益庞大。尽管提升这些模型能效的努力不断推进，但由于杰文斯悖论（Jevons Paradox）等回弹效应——即能效提升会推动使用量增加——这些努力并未转化为能源消耗的减少。因此，需要额外措施解决这一问题。我们提出，可行方向之一是采用生命周期思维，将LLMs视为可能成为废弃物的产品。基于此视角，我们研究了欧盟废弃物框架指令（EU's Waste Framework Directive）中的废弃物层级（waste hierarchy）的应用潜力，该指令提出了五种废弃物管理措施：预防（prevention）、再利用（reuse）、回收（recycling）、能量回收（recovery）和处置（disposal）。我们探讨了这些措施如何启发新的思维方式与方法，以减少LLMs废弃物及其总体环境影响。将废弃物层级应用于LLMs凸显出，预防废弃物对降低模型环境影响至关重要，主要是因为它减少了训练新模型的需求。预防可通过多种现有方法实现，包括再利用、“回收”及“能量回收”LLMs。此外，处置对节约能源和秉持对LLMs训练资源的审慎态度均具有重要意义。我们还强调，预防LLMs的不必要使用在降低模型气候影响方面具有巨大潜力。
英文摘要：
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.
