SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models
Language models for low-resource languages still make frequent grammatical errors. Alignment methods such as RLHF and DPO can correct them. However, they require human judgements, which are scarce for languages such as Icelandic. Automatic rewards offer an alternative, yet models can learn to exploit them without improving (reward hacking). We present SAGA (Score-weighted Adaptive Generation Alignment), which builds preference pairs using dependency parsers. Such parsers already exist for many low-resource languages. SAGA's reward combines parse quality with lexical diversity. It keeps only pairs whose reward gap reaches a threshold and trains on them with DPO. On Danish, Icelandic and No...
| Authors | Hoda Fakharzadehjahromy, Emil Wiman, Andreas Bueff et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 13 October 2026 |
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