压缩大模型可显著降耗，翻译质量仍达标。
The Hidden Costs of Translation Accuracy: Distillation, Quantization, and Environmental Impact
- 用蒸馏与量化压缩3.3B模型至600M，提升推理速度。
- 压缩后碳排放降低63%-65%，仅轻微影响翻译准确率。
- 适合关注绿色AI与实际部署的开发者与研究者。
大型语言模型的快速扩张引发了对其计算与环境成本的关注。本研究以机器翻译为例，对比全尺寸、蒸馏及量化模型在翻译质量与效率间的权衡。在Flores+基准和法语、印地语、卡纳达语对话翻译的人工评估中发现，3.3B FP32全模型虽取得最高BLEU分数，但每运行一次碳排放约0.007-0.008公斤。600M FP32蒸馏模型将推理时间减少71%-78%，碳排放降低63%-65%，且BLEU得分下降极小。人工评估显示，即使采用极端量化（INT4），翻译准确性与流畅性仍保持高水平，各模型间差异较小。结果表明，模型压缩能大幅降低计算需求与环境影响，同时维持竞争力，但在低资源语言中权衡更明显。呼吁在自然语言处理中将效率与可持续性作为核心评价维度。
原文摘要 · Abstract (English)
The rapid expansion of large language models (LLMs) has heightened concerns about their computational and environmental costs. This study investigates the trade-offs between translation quality and efficiency by comparing full-scale, distilled, and quantized models using machine translation as a case study. We evaluated performance on the Flores+ benchmark and through human judgments of conversational translations in French, Hindi, and Kannada. Our analysis revealed that the full 3.3B FP32 model, while achieving the highest BLEU scores, incurred the largest environmental footprint (~ 0.007-0.008 kg CO2 per run). The distilled 600M FP32 model reduced inference time by 71-78% and carbon emissions by 63-65% compared with the full model, with only minimal reductions in BLEU scores. Human evaluations further showed that even aggressive quantization (INT4) preserved high levels of accuracy and fluency, with differences between models generally minor. These findings demonstrate that model compression strategies can substantially reduce computational demands and environmental impact while maintaining competitive translation quality, though trade-offs are more pronounced in low-resource settings. We argue for evaluation frameworks that integrate efficiency and sustainability alongside accuracy as central dimensions of progress in NLP.
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