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%\title{Enhancing indigenous Language Translation with Synthetic Data Augmentation}
%\title{Language-Specific Preprocessing and Synthetic Data Augmentation for Low-Resource indigenous Machine Translation}
\title{Retrieval-Augmented Long-Context Translation for Cultural Image Captioning: Gators submission for AmericasNLP 2026 shared task}

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\author{
  Aashish Dhawan \\
  University of Florida \\
  \texttt{aashish.dhawan@ufl.edu}
  \And
  Christopher Driggers-Ellis \\
  University of Florida \\
  \texttt{driggersellis.cw@ufl.edu}
  \And
  Dzmitry Kasinets \\
  University of Florida \\
  \texttt{dkasinets@ufl.edu}
  \AND
  % Sanya Chaturvedi \\
  % University of Florida \\
  % \texttt{sanyachaturvedi@ufl.edu}
  Christan Grant \\
  University of Florida \\
  \texttt{christan@ufl.edu}
  \And
  Daisy Wang \\
  University of Florida \\
  \texttt{daisyw@cise.ufl.edu}
}

\begin{document}
\maketitle
% \begin{abstract}
% Low-resource languages often lack the high-quality parallel corpora required for robust neural machine translation (NMT). Synthetic data generation offers a promising solution to overcome these limitations. In this study, we augment a curated dataset with synthetic parallel data generated using a state-of-the-art machine translation model. We fine-tune the mBART model on the combined dataset and evaluate translation quality using BLEU and ChrF metrics. In addition, we describe our preprocessing pipeline, which includes language-specific cleaning, token normalization, and rare token replacement, to ensure high-quality input data. Our experiments demonstrate that incorporating generated data significantly improves translation performance—especially in capturing culturally nuanced expressions—thus providing a viable pathway for improving NMT in low-resource scenarios.
% \end{abstract}
\begin{abstract}
We present the University of Florida Gators submission to the Guaraní track of the AmericasNLP 2026 shared task on cultural image captioning for Indigenous languages. Our system uses a two-stage pipeline: a vision model first generates a Spanish caption, and a retrieval-augmented large language model then produces the final Guaraní caption using Spanish–Guaraní exemplars. We compare this approach against an inherited mBART baseline and a range of prompting configurations, varying model family, retrieval size, and exemplar strategy. OpenAI-based systems provide only modest gains over the baseline, while Gemini 2.5 Flash yields much stronger improvements. We also find that using development references from the same evaluation pool as in-context exemplars can substantially overstate performance. To address this, we separate contaminated same-pool results from a cleaner cross-half split evaluation. Under this split protocol, our best Gemini 2.5 Flash system reaches 41.48 chrF++, compared with 31.76 without development exemplars and 22.40 for the mBART baseline. These results show that long-context in-context translation is highly effective for low-resource Indigenous caption generation, but that careful evaluation is essential when development references are reused as prompts.
\end{abstract}




% \section{Introduction}
% The world faces a critical challenge in the alarming rate at which languages are disappearing, with indigenous languages being particularly vulnerable \cite{mager2023ethical}. This loss extends beyond mere words, encompassing the erosion of unique cultural heritage and invaluable traditional knowledge \cite{woodbury2012endangered}. Aymara and Guarani, while spoken by a significant number of people in the Americas, are not immune to these challenges. The intricate structures of these languages, coupled with a scarcity of readily available digital resources, pose considerable obstacles to the development of effective machine translation (MT) systems \cite{rodriguez2022challenges}. Projections indicate that a substantial proportion of the world's currently spoken languages could face severe endangerment or extinction by the close of this century, underscoring the urgency of research aimed at preserving and facilitating communication in these languages \cite{woodbury2012endangered}.
% Traditional MT methodologies, which heavily depend on the existence of large parallel corpora, often prove inadequate for low-resource languages like Aymara and Guarani. However, recent progress in neural machine translation (NMT), along with techniques such as data augmentation and cross-lingual transfer learning, offers promising avenues for addressing these limitations. This paper explores the potential of synthetic data augmentation, drawing inspiration from the successful MultiScript30k project and directly contributing to the aims of initiatives like AmericasNLP \cite{mager2023ethical}. AmericasNLP organizes shared tasks specifically designed to foster advancements in MT for the indigenous languages of the Americas \cite{ebrahimi2023findings}. Our investigation focuses on the hypothesis that augmenting existing datasets with parallel data generated synthetically can enhance the performance of the multilingual mBART model in translating between these indigenous languages and a higher-resource language, Spanish. The methodology employed involves fine-tuning the mBART model on both curated and synthetically augmented datasets, with performance evaluated using the widely recognized BLEU and chrF++ scores. Ultimately, this research aims to demonstrate the potential of synthetic data to improve translation quality in these challenging low-resource scenarios, thereby contributing to the broader efforts in language preservation and supporting the goals of initiatives such as AmericasNLP\cite{ebrahimi2023findings}.

\section{Introduction}
The AmericasNLP 2026 shared task asks participants to generate culturally grounded captions for images in Indigenous languages of the Americas (AmericasNLP 2026a). The task is challenging for at least three reasons. First, the target languages are low-resource. Second, the captions are culturally specific rather than generic visual descriptions. Third, the task requires not only lexical transfer, but also stylistic control: successful systems must produce short, natural captions that match the reference register expected by the organizers.

Our work builds directly on Dhawan et al. \cite{dhawan-etal-2026-improving}, which showed that synthetic parallel data and language-specific preprocessing improve low-resource Indigenous machine translation, including Guaraní. That earlier work targeted sentence-level translation and found that curated-plus-synthetic training improved Guaraní translation with an mBART-based system. In the 2026 shared task, however, a standard neural machine translation pipeline proved insufficient. When we applied our existing Guaraní translation model to Spanish intermediate captions produced from the images, the resulting captions were often fluent enough at the sentence level but mismatched the target caption register. In other words, the bottleneck shifted from generic translation quality to domain adaptation and culturally grounded caption style.

To address this mismatch, we moved from fine-tuned sequence-to-sequence translation toward retrieval-augmented in-context translation with large language models. The core idea is simple: instead of relying on a single model fine-tuned on mixed-domain Guaraní corpora, we retrieve Spanish–Guaraní examples that are similar to the current caption and provide them as in-context demonstrations. This design lets the decoder adapt its lexical choices and stylistic register at inference time. We evaluate multiple OpenAI and Gemini models, vary the number of retrieved training examples and development exemplars, and test prompt variants and retrieval heuristics.

Our experiments lead to three main findings. First, among OpenAI models, many-shot direct translation outperforms both our mBART baseline and LLM post-correction, but gains are modest and highly sensitive to prompt composition. Second, Gemini 2.5 Flash is dramatically stronger than the GPT-family models we tested in this setting. Third, using development references as in-context exemplars can create deceptively high development scores if evaluation is not carefully controlled. We therefore distinguish between same-pool development results, which are useful for model selection but inflated, and a cross-half split protocol that provides a more credible estimate of generalization.

The main contributions of this paper are the following. We present a retrieval-augmented LLM caption translation pipeline for Guaraní cultural image captioning. We document an extensive set of negative and positive ablations, including model substitutions, prompt revisions, reranking, and development-exemplar sweeps. Finally, we show that evaluation design is itself a central methodological issue for dev-conditioned in-context learning, and we provide a more conservative split-dev estimate for our best system.

\section{Background and Related Work}
Machine translation for Indigenous languages of the Americas has advanced largely through the AmericasNLP shared tasks, which have established evaluation benchmarks and made parallel corpora available for many low-resource languages \cite{mager2021findings, ebrahimi-etal-2023-findings, ebrahimi2024findings}. Strong systems in these shared tasks have typically relied on multilingual pretrained models such as mBART, M2M-100, or NLLB-200, often combined with synthetic data generation, multilingual transfer, and additional corpus collection\cite{gow2023sheffield, tonja2023enhancing, costa2022no}. A recurring pattern in this literature is that performance improvements come not only from larger models, but also from better alignment between model capacity, augmentation strategy, and the target domain.

Our work is a direct continuation of Dhawan et al. \cite{dhawan-etal-2026-improving}, which studied Indigenous machine translation with synthetic data augmentation and language-specific preprocessing. That paper showed that curated-plus-synthetic training improved Guaraní and Quechua translation with an mBART-based approach, and it emphasized the importance of language-aware normalization for morphologically rich and orthographically variable settings. The current paper extends that line of work from text-to-text MT to cultural image captioning, where the main challenge is no longer generic translation alone but also stylistic adaptation to a narrow caption register.

Character-based evaluation metrics are also especially relevant in this area. chrF and chrF++ are widely used for morphologically rich languages because they are more robust than BLEU to inflectional variation and spelling differences\cite{popovic-2015-chrf, popovic-2017-chrf}. The AmericasNLP 2026 organizers likewise use chrF++ as the first-stage ranking metric for the shared task.

Finally, our work relates to the broader use of in-context learning for low-resource generation. Instead of relying exclusively on fixed model parameters after fine-tuning, retrieval-augmented prompting can adapt the decoder to the current example at inference time. In our setting, this is especially attractive because the target outputs are short and stylistically constrained: parallel demonstrations can serve not only as semantic guides, but also as direct evidence of the desired caption register.

\section{Dataset and Methodology}
\subsection{Task Data}

%\input{tables/data_stats}
We focus on the Guaraní track of the AmericasNLP 2026 shared task. The official development set contains 50 examples, and the organizers release the data in JSONL format paired with images. The pilot set includes Spanish captions for reference, but the organizers explicitly note that these are pilot-only and will not be present in development or test, so they should not be relied upon for system building.

Our final submission pipeline is therefore two-stage. Stage 1 produces a Spanish caption from the image using Qwen 2.5B\cite{bai2025qwen25vl}. Stage 2 maps that Spanish caption into Guaraní. In the experiments reported here, we hold the Spanish captions fixed and optimize only the Guaraní translation stage. This lets us analyze the translation component independently of the image captioner and makes the ablations directly comparable.



\subsection{Retrieval Bank}
The translation stage uses a Spanish–Guaraní retrieval bank of 53,183 parallel pairs. This bank combines curated Guaraní–Spanish training data from americasNLP2023 \cite{ebrahimi2023findings} with synthetic parallel data from MultiScript30k project\cite{driggersellis2025multiscript30kleveragingmultilingualembeddings}. The resource composition follows the general approach introduced in Dhawan et al. \cite{dhawan-etal-2026-improving}, where synthetic augmentation improved Guaraní MT and increased the number of usable training pairs substantially.

For retrieval, we index the Spanish side of this parallel corpus with BM25. At inference time, each Spanish caption is used as a query, and the top-$k$ retrieved Spanish–Guaraní pairs are inserted into the prompt as parallel demonstrations. In some configurations, we also add development exemplars chosen from the shared-task development set.

\subsection{Baseline and LLM Systems}


\paragraph{Guarani.}  
Our baseline is the Guaraní translation model inherited from Dhawan et al. (2026), based on mBART and applied to the fixed Spanish intermediate captions. We treat this as the strongest conventional translation baseline available in our pipeline.

We then test a family of retrieval-augmented LLM systems. The OpenAI experiments use GPT-4o-mini, GPT-4.1-mini, and GPT-4.1. For these models, we vary the number of retrieved training examples, the number of development exemplars, and the prompt formulation. We also test a post-correction approach that asks the LLM to revise the mBART output rather than translate directly.

Finally, we port the same prompting framework to Gemini 2.5 Flash. This turns out to be the strongest model family in our experiments.

\paragraph{Wixarika.}


\subsection{Model Fine-tuning}
We fine-tune the multilingual mBART model (\texttt{facebook/mbart-large-50}) using the HuggingFace Transformers library. mBART is pretrained using a denoising autoencoding objective across 50 languages, enabling effective cross-lingual transfer in low-resource translation scenarios \cite{tang2020multilingual}. We use the \texttt{MBart50Tokenizer} with Spanish (\texttt{es\_XX}) as the source language and the appropriate target language tags (\texttt{aym\_XX} for Aymara and \texttt{gn\_XX} for Guarani).

To better support Guarani orthography, we extend the tokenizer vocabulary with Guarani-specific characters and frequent multi-character units, including nasal vowels and diacritics (\textit{\~{a}, \~{e}, \~{\i}, \~{o}, \~{u}}), the combining tilde, and common digraphs (\textit{ch, mb, ng}). After extending the tokenizer, the model’s embedding matrix is resized accordingly.


For Aymara--Spanish translation, we train using a learning rate of $2\times10^{-5}$, batch size 16, gradient accumulation of 4, and 20 epochs. For Guarani--Spanish translation, we use a learning rate of $3\times10^{-5}$, batch size 8, gradient accumulation of 4, and 15 epochs. All models are optimized using AdamW with a cosine learning rate schedule and warm-up. Early stopping with a patience of three evaluation intervals based on validation BLEU is applied to prevent overfitting. These configurations are consistent with recent AmericasNLP submissions \cite{gow2023sheffield, tonja2023enhancing}.

\subsection{Prompting Strategy}
The prompt structure is deliberately simple. The system prompt tells the model to translate from Spanish into natural Guaraní/Jopara, match the style of the examples, stay concise, preserve culturally specific nouns where appropriate, and produce exactly one line. The user prompt then presents two sections: a block of optional development exemplars and a block of retrieved Spanish–Guaraní parallel examples, followed by the current Spanish caption.

This design was chosen after several failed prompt-engineering attempts. More aggressive instructions intended to suppress generic lead-ins or force noun-first phrasing reduced performance rather than improving it. Our final prompts therefore keep the system instruction minimal and let the retrieved demonstrations carry most of the stylistic signal.

\subsection{Evaluation Design}


\section{System Description}
Our submission system is a retrieval-augmented long-context translator embedded in a two-stage captioning pipeline.

Given an image, Stage 1 generates a Spanish caption. We treat this stage as a separate visual captioning component and do not optimize it extensively in this paper. Stage 2 then receives the Spanish caption and performs Guaraní generation using an LLM conditioned on two forms of evidence: retrieved Spanish–Guaraní parallel pairs from the training bank and, when allowed by the configuration, a set of development exemplars.

For each query caption, BM25 retrieval is performed over the Spanish side of the 53,183-pair bank. We tested several retrieval sizes. Among OpenAI models, retrieval size had a narrow optimum around 28 examples, with both smaller and larger values performing worse. Gemini was far more robust to larger context windows, and our strongest final submission recipe uses 80 retrieved pairs.

Development exemplars serve a different role from retrieved training pairs. Retrieved pairs provide semantic and lexical grounding from the translation bank, whereas development exemplars provide direct evidence of the target caption register used by the task. This distinction is crucial. In the best-performing Gemini configurations, development exemplars contribute a large part of the gain, but they also create evaluation risk if drawn from the same pool as the examples being scored.

We tested one heuristic reranker that attempted to improve prompt quality by preferring shorter, more caption-like retrieved pairs while penalizing place names and boilerplate. This consistently hurt performance, suggesting that simple hand-designed notions of “good caption examples” were inferior to straightforward BM25 relevance for this task.

At submission time, our chosen inference recipe is Gemini 2.5 Flash with 80 retrieved pairs, 49 development exemplars, no reranking, and temperature 0.0. This is the strongest test-time configuration we found. For scientific reporting, however, we rely on the cleaner cross-half split results rather than the inflated same-pool full-dev numbers.


\section{Results}
\subsection{Main Experimental Progression}
\input{tables/result_gua}
Table \ref{tab:dev_results} summarizes the main progression of experiments on the fixed Spanish development captions.
Several trends emerge immediately. First, direct many-shot translation is better than post-correction, confirming that it is more effective to generate the caption in one step than to repair the mBART output after the fact. Second, among the OpenAI models we tested, GPT-4o-mini remains the strongest, but the gains over the mBART baseline are modest. Third, Gemini 2.5 Flash yields much larger improvements, even before adding any development exemplars.


\subsection{OpenAI Ablations}
Before moving to Gemini, we mapped the search space with OpenAI models. The best OpenAI setting used GPT-4o-mini with 28 retrieved examples and 4 development exemplars, reaching 23.60 chrF++. Reducing the number of retrieved pairs to 20 or 16 hurt, while increasing it to 32 or 40 also hurt. Removing development exemplars reduced performance, and increasing them from 4 to 8 also reduced performance. GPT-4.1-mini and GPT-4.1 were both competitive but did not surpass GPT-4o-mini.

This pattern suggests that, for the OpenAI models we tested, the in-context regime has a narrow optimum. Too little context leaves the model underconstrained; too much context appears to add noise or dilute the style signal.

\subsection{Negative Prompting and Reranking Results}
We also tested several interventions that looked reasonable but were consistently negative.

A prompt rewrite aimed at suppressing generic scene-introduction phrases reduced performance substantially. A retrieval reranker that attempted to prefer short caption-like pairs also reduced performance, both for OpenAI and Gemini. Likewise, using 49 same-pool development exemplars with GPT-4o-mini degraded performance instead of improving it.

These results matter because they show that this task does not respond well to aggressive heuristic control. The most effective systems were built by keeping the instruction stable and varying only model choice and the amount of evidence supplied.

\section{Analysis}
We probably should put this as appendix? different models we tried and results
\section{Discussion}

\section{Future Work}

\section{Conclusion}

\section{Limitations}





% \section{Acknowledgments}


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