Machine Translation Using Grammar Materials for LLM Post-Correction

AbstractThis paper describes George Mason University’s submission to the AmericasNLP 2025 Shared Task on Machine Translation into Indigenous Languages. We prompt a large language model (LLM) with grammar reference materials to correct the translations produced by a finetuned Encoder-Decoder machine translation system. This system leads to improvements when translating from the indigenous languages into Spanish indicating that LLMs are capable of using grammar materials to decipher an unseen language.