This paper presents the findings of the AmericasNLP 2025 Shared Tasks: (1) machine translation for truly low-resource languages, (2) morphological adaptation for generating educational examples, and (3) developing metrics for machine translation in Indigenous languages. The shared tasks cover 14 diverse Indigenous languages of the Americas. A total of 11 teams participated, submitting 26 systems across all tasks, languages, and models. We describe the shared tasks, introduce the datasets and evaluation metrics used, summarize the baselines and submitted systems, and report our findings.

The use of machine learning and Natural Language Processing (NLP) technologies can assist in the preservation and revitalization of indigenous languages, particularly those classified as “low-resource.” Given the increasing digitization of information, the development of translation tools for these languages is of significant importance. These tools not only facilitate better access to digital resources for indigenous communities but also stimulate language preservation efforts and potentially foster more inclusive, equitable societies, as demonstrated by the AmericasNLP workshop since 2021. The focus of this paper is Colombia, a country home to 65 distinct indigenous languages, presenting a vast spectrum of linguistic characteristics. This cultural and linguistic diversity is an inherent pillar of the nation’s identity, and safeguarding it has been increasingly challenging given the dwindling number of native speakers and the communities’ inclination towards oral traditions. Considering this context, scattered initiatives exist to develop translation systems for these languages. However, these endeavors suffer from a lack of consolidated, comparable data. This paper consolidates a dataset of parallel data in four Colombian indigenous languages - Wayuunaiki, Arhuaco, Inga, and Nasa - gathered from existing digital resources. It also presents the creation of baseline models for future translation and comparison, ultimately serving as a catalyst for incorporating more digital resources progressively.