小模型在多数任务中实现低排放高效率，助力绿色AI发展。
Emissions and Performance Trade-off Between Small and Large Language Models
- 用微调小模型替代大模型进行特定任务
- 六项任务中四项表现相当，推理碳排放显著降低
- 适合关注环保与能效的AI应用开发者
大型语言模型（LLMs）的训练和推理过程能耗巨大，引发对碳足迹的担忧。本研究探讨微调的小型语言模型（SLMs）作为预设任务的可持续替代方案。我们对自然语言处理、推理和编程三类任务中的LLMs与微调后的SLMs进行了对比分析。结果显示，在六项任务中的四项，SLMs在推理阶段实现了与LLMs相当的性能，同时碳排放大幅下降。研究证明，小模型在降低资源密集型模型环境影响方面具有可行性，有助于推动绿色人工智能的发展。
原文摘要 · Abstract (English)
The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analysis of the performance-emissions trade-off between LLMs and fine-tuned SLMs across selected tasks under Natural Language Processing, Reasoning and Programming. Our results show that in four out of the six selected tasks, SLMs maintained comparable performances for a significant reduction in carbon emissions during inference. Our findings demonstrate the viability of smaller models in mitigating the environmental impact of resource-heavy LLMs, thus advancing towards sustainable, green AI.
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