Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

ArXi:2603.16660v1 Announce Type: cross Large Language Models (LLMs) have achieved strong performance across many downstream tasks, yet their effectiveness in extremely low-resource machine translation remains limited. Standard adaptation techniques typically rely on large-scale parallel data or extensive fine-tuning, which are infeasible for the long tail of underrepresented languages.