Indigenous languages face significant challenges due to their endangered status and limited resources which makes their integration into NLP systems difficult. This study investigates the use of Large Language Models (LLMs) for sentence transformation tasks in Indigenous languages, focusing on Bribri, Guarani, and Maya. Here, the dataset from the AmericasNLP 2025 Shared Task 2 is used to explore sentence transformations in Indigenous languages. The goal is to create educational tools by modifying sentences based on linguistic instructions, such as changes in tense, aspect, voice, person, and other grammatical features. The methodology involves preprocessing data, simplifying transformation tags, and designing zero-shot and few-shot prompts to guide LLMs in sentence rewriting. Fine-tuning techniques like LoRA and Bits-and-Bytes quantization were employed to optimize model performance while reducing computational costs. Among the tested models, Llama 3.2(3B-Instruct) demonstrated superior performance across all languages with high BLEU and ChrF++ scores, particularly excelling in few-shot settings. The Llama 3.2 model achieved BLEU scores of 19.51 for Bribri, 13.67 for Guarani, and 55.86 for Maya in test settings. Additionally, ChrF++ scores reached 50.29 for Bribri, 58.55 for Guarani, and 80.12 for Maya, showcasing its effectiveness in handling sentence transformation. These results highlight the potential of LLMs that can improve NLP tools for indigenous languages and help preserve linguistic diversity.

This paper presents our approach to machine translation between Spanish and 13 Indigenous languages of the Americas as part of the AmericasNLP 2025 shared task. Addressing the challenges of low-resource translation, we fine-tuned advanced multilingual models, including NLLB-200 (Distilled-600M), Llama 3.1 (8B-Instruct) and XGLM 1.7B, using techniques such as dynamic batching, token adjustments, and embedding initialization. Data preprocessing steps like punctuation removal and tokenization refinements were employed to achieve data generalization. While our models demonstrated strong performance for Awajun and Quechua translations, they struggled with morphologically complex languages like Nahuatl and Otomí. Our approach achieved competitive ChrF++ scores for Awajun (35.16) and Quechua (31.01) in the Spanish-to-Indigenous translation track (Es→Xx). Similarly, in the Indigenous-to-Spanish track (Xx→Es), we obtained ChrF++ scores of 33.70 for Awajun and 31.71 for Quechua. These results underscore the potential of tailored methodologies in preserving linguistic diversity while advancing machine translation for endangered languages.