Get Started
Home
Topics
Search
Library
Research questionHow can we translate instruction-tuning data without corrupting task constraints or required outputs?Translated instruction examples can change task-critical constraints and required outputs even when their wording remains fluent. Models trained on such data may improve on surface-level text metrics while following instructions less reliably.
AI
Evaluation & Benchmarks
LLM Pretraining & Post-training
Natural Language Processing
Latest papersRecent research connected to this question, newest first.EuroAlpaca: Task-Preserving Localisation of Instruction Data for European LanguagesThe source covers 50 European languages and regional varieties, with evidence from LoRA experiments involving four language models and the Aya Evaluation Suite and European-IFEval benchmark. Its findings are limited to the described localization resource, training setup, languages, models, and evaluations.research paper · Sep 4, 2026
Related questions
How can instruction-tuned LLMs learn corpus-specific knowledge without exhaustive synthetic QA or instruction fine-tuning?How can language models reliably follow instructions containing many simultaneous constraints?How can models adapt to low-resource languages without damaging source-language and related-task performance?How can multilingual question answering remain consistent across languages without erasing culturally appropriate differences?