Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following
Elastic Weight Consolidation limits general forgetting in translation fine-tuning but not translation-specific instruction following.
The authors test whether general catastrophic-forgetting mitigations preserve machine-translation instruction following, including formality, grammatical gender, and length control. Screening used Llama 3.2 1B Instruct, followed by Llama 3.1 8B Instruct fine-tuned on bidirectional Arabic-English or Spanish-English data. Elastic Weight Consolidation preserved general benchmarks best: on the 8B Spanish model, the average general score fell 1.7 points versus 11.0 for standard fine-tuning, while formality and gender scores stayed close to standard fine-tuning. Only mixing in control-task examples preserved those controls, and the gains did not transfer to unseen prompts for the same task.
- Study covers formality, grammatical gender, and length-control instructions.
- Tests use Llama 3.2 1B and Llama 3.1 8B Instruct.
- EWC cut an 11.0-point general-score drop to 1.7 on 8B Spanish.
- Only control-example mixing preserved controls, and not on unseen prompts.
From the original article
1 min read at arxiv.orgFine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general benchmarks.
By Niklas Scholz · arxiv.org
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