发表机构
Australian National University; Commonwealth Scientific and Industrial Research Organisation（澳大利亚国立大学; 联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AI研究缺乏标准化环境影响指标的问题，本文提出可持续性指标、碳成本估算启发式方法及carbonbenchmark工具，并引入SMAJ框架，以推动在追求精度的同时兼顾计算效率与环境责任。
AI 中文摘要
随着大型语言模型（LLM）的能力和普及程度不断提高，其环境足迹也随之增加。尽管有负责任的AI的呼吁，机器学习社区仍缺乏标准化的碳核算实践。我们对NeurIPS 2025接收的5,285篇论文进行的自动化文献综述显示，环境影响的报告几乎不存在。为了推动向可持续AI的转变，我们定义了用于评估模型训练效率的标准化可持续性指标，并辅以估算LLM推理碳成本的简单启发式方法。我们在carbonbenchmark中实现了这些指标，这是一个用于跟踪和报告排放的即插即用软件解决方案。最后，为了遏制以不成比例的环境成本追求边际精度提升的做法，我们正式提出了“以最小模型完成任务”（SMAJ）框架，该框架挑战该领域在传统“最先进”（SotA）精度的同时，优先考虑计算效率和环境责任。
英文摘要
As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practices for carbon accounting. Our automated literature review of the 5,285 papers accepted to NeurIPS 2025 reveals that reporting of environmental impact is nearly non-existent. To catalyse a shift toward sustainable AI, we define standardised sustainability metrics for evaluating model training efficiency, accompanied by simple heuristics to estimate the carbon cost of LLM inference. We implement these metrics in carbonbenchmark, a drop-in software solution for tracking and reporting emissions. Finally, to combat the pursuit of marginal accuracy gains at disproportionate environmental costs, we formalise the `Smallest Model that Achieves the Job' (SMAJ), a framework which challenges the field to prioritise computational efficiency and environmental accountability alongside traditional `State-of-the-Art' (SotA) accuracy.
