用质量评估决定何时调用大模型，省算力还保效果
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral
- 用现有质量评估指标做分流决策，小模型先处理
- 仅30%~50%的翻译调用大模型，性能接近单独用大模型
- 适合追求效率的机器翻译部署场景
大模型通常比小模型表现更好，但计算成本高。级联机制提供一种解决方案：默认使用小模型，仅将部分样本转发给更强大的大模型。然而，设计有效的转发规则仍具挑战。本文提出一种简单有效的方法，利用现有的质量估计（QE）指标作为转发依据。实验表明，基于QE的级联系统可在仅调用大模型30%至50%的实例情况下，达到与单独使用大模型相当的性能，显著降低计算开销。该方法通过自动评估和人工评估双重验证。
原文摘要 · Abstract (English)
Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some instances to larger, more powerful models. However, designing effective deferral rules remains a challenge. In this paper, we propose a simple yet effective approach for machine translation, using existing quality estimation (QE) metrics as deferral rules. We show that QE-based deferral allows a cascaded system to match the performance of a larger model while invoking it for a small fraction (30% to 50%) of the examples, significantly reducing computational costs. We validate this approach through both automatic and human evaluation.
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