What we mean by dataset in the AI context
A dataset is a structured collection of data that serves as the fundamental raw material for building artificial intelligence (AI) systems. In the field of natural language processing (NLP) —the discipline that enables machines to understand and generate human language—, this data takes the form of texts, dialogues, or instructions. Its function is to allow AI models to identify patterns and learn to perform specific tasks, from translating a document to maintaining a fluid conversation.
Historically, the focus of technological development centered on the model's architecture (its internal structure and computing capacity). However, today we know that the behavior and success of an AI depend not only on its design, but directly on the data it is trained on. This relationship is so close that the model becomes a reflection of its dataset: if the data is biased or incomplete, so will the AI be (Bender et al., 2021; Paullada et al., 2021).
This link has driven a paradigm shift toward quality over quantity. Processing massive volumes of information is no longer enough; for a model to be effective, datasets must be relevant, high-quality, and aligned with the language and cultural context of the target market (Blasi et al., 2022; Kreutzer et al., 2022). Thus, the dataset has ceased to be a secondary technical component and has become the piece that truly determines the proper functioning of an AI.
The dataset as a guide: training and evaluation
In the development of an AI, the dataset serves a dual function: it is the textbook from which the model learns and, at the same time, the exam by which its success is measured. If the book contains errors or the exam is too simple, the resulting model will be unreliable in real-world environments.
During training, the model internalizes patterns and behaviors based on the data it receives. If this data has not been thoroughly reviewed or lacks cultural nuances, the AI will simply replicate and amplify those flaws. Once trained, the evaluation phase determines whether the model is ready for the market. Here, the use of generic or «contaminated» datasets (data the model has already seen before) can create a false sense of accuracy, hiding limitations that only expert linguists can detect (Dong et al., 2024; Samuel et al., 2024).
Ultimately, the model is a reflection of the dataset that feeds it. If we understand data as the foundation of its reasoning, it seems evident that translating it into other languages cannot be left to chance. For an AI to be truly effective in a new environment, translating texts word for word is not enough; they must be adapted so that the AI understands the local context and behaves as expected.
Why datasets require a specific translation approach
At first glance, translating a dataset may seem similar to any other linguistic project. However, while in a traditional translation we work with coherent texts and clear objectives, datasets tend to be fragmented collections of data. This structure presents challenges that conventional methods cannot always resolve:
- Limited context. Many datasets contain isolated sentences without additional information about who is speaking, with what intention, or in what situation. This requires interpreting the function of each segment so that the model learns the appropriate response in the target language.
- Non-translatable technical elements. It is common to encounter code fragments, variables, placeholders, or tags. Identifying which parts must be translated and which must remain intact is vital for the dataset to remain functional after the process.
- Consistency amplification. Decisions that in other texts would merely be stylistic (such as the use of formal or informal address), are here reproduced on a large scale. If strict consistency is not maintained, the model may internalize inconsistent patterns.
- Domain diversity. In a single project, customer service dialogues may be combined with medical queries or technical instructions. This variety limits the use of traditional translation memories and requires constant adaptation to the register and subject matter of each data point.
- Fidelity vs. correction. Unlike editorial translation, it is sometimes necessary to preserve grammatical errors or informal language from the original. «Improving» the text can be counterproductive if the goal is for the model to learn to identify or handle real user language.
Localizing datasets: the value of cultural adaptation
In this context, localization emerges as the natural response. The goal is not only to transfer words, but to adjust the dataset so that the resulting model behaves organically in the target market.
Localizing a dataset involves, for example, adapting cultural references, brands, or institutions that do not exist in the target culture. It also means adjusting local elements such as date formats, currencies, or units of measurement, and reflecting the social conventions of each region.
In the case of large language models (LLMs), this approach is fundamental. It is not about producing a static final text, but about teaching the model to interact correctly in a given language. A well-localized dataset ensures that the AI not only speaks the language, but is coherent and aligned with the expectations of its future users.
Preparation and validation: ensuring the integrity of the dataset
Before beginning the translation, the preparation phase is fundamental. In projects of this volume, a poorly defined criterion at the outset can lead to hundreds of hours of subsequent correction. For this reason, it is essential to establish in advance:
- Linguistic and technical criteria. Defining the level of literalness, the use of regional variants, or the treatment of gender, as well as identifying which elements (code, tags, or variables) must remain intact.
- Error management. Deciding which ambiguities or grammatical errors in the original must be preserved so as not to alter the pedagogical value of the data.
- Specific resources. Beyond conventional style guides, it is necessary to have controlled terminology glossaries and quality assurance (QA) tools adapted to large data volumes.
Once translated, quality checking also requires a different approach. A traditional human review is not enough; the validation of a dataset combines specialized linguistic reviews with automatic consistency checks and statistical sampling. At its core, this process resembles data quality control more than classic editorial proofreading.
Our methodology in dataset localization
At imaxin, we have over 25 years of experience in software localization and large-volume multilingual content translation, working in environments where context is limited, consistency is critical, and every linguistic decision has a direct impact on the final product's performance.
This expertise has allowed us to evolve toward the translation and curation of datasets, combining human judgment with the power of technology. We apply the same quality standards that have defined us for decades, relying on the most advanced computer-assisted translation (CAT) tools and machine translation (MT) systems available on the market. With a clear methodology from the outset, we ensure that each data point is accurate, consistent, and functional in any language.
Bibliography
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11).
Blasi, D., Anastasopoulos, A., & Neubig, G. (2022, May). Systematic inequalities in language technology performance across the world's languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 5486-5505).
Lavie, A., Hanneman, G., Agrawal, S., Kanojia, D., Lo, C. K., Zouhar, V., ... & Deutsch, D. (2025, November). Findings of the WMT25 shared task on automated translation evaluation systems: Linguistic diversity is challenging and references still help. In Proceedings of the Tenth Conference on Machine Translation (pp. 436-483).
Kocmi, T., Artemova, E., Avramidis, E., Bawden, R., Bojar, O., Dranch, K., ... & Zouhar, V. (2025, November). Findings of the wmt25 general machine translation shared task: Time to stop evaluating on easy test sets. In Proceedings of the Tenth Conference on Machine Translation (pp. 355-413).
Dong, Y., Jiang, X., Liu, H., Jin, Z., Gu, B., & Yang, M., & Li, G. (2024). Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models. Findings of the Association for Computational Linguistics: ACL 2024.
Samuel, V., Zhou, Y., & Zou, H. P. (2025). Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges. Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025).
Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2025). Datasets for large language models: A comprehensive survey. Artificial Intelligence Review, 58(12), 403.
Kenny, D. (2022). Human and machine translation. Machine translation for everyone: Empowering users in the age of artificial intelligence, 18, 23.
