这篇论文用依赖解析器代替人工标注做偏好优化，北欧三种低资源语言上效果提升明显，做多语言模型的人可以看看。
SAGA是一个用依赖解析器替代人工偏好标注的偏好优化框架，面向形态丰富但标注稀缺的低资源语言。它把解析器判断转换为delta-DPO偏好对，并用解析质量与词汇多样性的复合奖励进行优化。在丹麦语、冰岛语和挪威博克马尔语上基于GPT-SW3-1.3B测试，丹麦语解析成功率从69.0%升至93.8%，冰岛语独立评测提升4.5个百分点，挪威博克马尔语提升28个百分点。结果表明，在高质量解析器可用的低资源语言中，解析器监督可成为人工偏好标注的实用替代方案。
SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models
Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations. Extending these methods to morphologically rich, low-resource languages remains challenging because such annotations are scarce. We present SAGA (Score-weighted Adaptive Generation Alignment), a parser-guided preference optimisation framework that replaces human labels with dependency-parser supervision. SAGA converts parser judgements into preference pairs for delta-DPO, combines parser quality with lexical diversity in a composite reward, filters low-information pairs using a reward-gap criterion, and monitors reward hacking to maintain reliable supervision. Across Danish, Icelandic, and Norwegian Bokmål using GPT-SW3-1.3B, SAGA consistently improves grammatical quality without requiring human preference labels. Danish parse success increases from 69.0% to 93.8%, Icelandic achieves a +4.5 percentage-point improvement on an independent Stanza evaluation (three-run mean +3.3 percentage points) while native speakers prefer SAGA outputs in 80% of pairwise comparisons, and Norwegian Bokmål improves by +28 percentage points. These results demonstrate that parser-derived supervision is a practical alternative to human preference annotation for grammatical alignment in low-resource languages where high-quality dependency parsers are available.