Leveraging LLM s for Cross-Locale Adaptation: a Workflow Proposal on S panish Variants (2025)

Abstract: Localization strategies can differ widely between languages, but the necessity and efficiency of maintaining distinct strategies for closely related variants of the same language is debatable. This paper explores the potential for unifying localization strategies across different Spanish locales, leveraging Large Language Models, prompting techniques, and specialized linguistic resources to perform cross-locale adaptations from a chosen baseline. In this study, we examine and develop vocabulary, terminology, grammar, and style transformation methods from Latin American into Mexican and Argentine Spanish. Our findings suggest that parting from a core translation and then following an automated adaptation process to unify localization strategies is feasible for Spanish diverse variants, regardless of the type of divergence each of them has from the baseline locale. However, even if the need for human post-editing is then minimal compared to a fully ‘manual’ cross-locale adaptation, the linguistic review remains crucial, particularly for editing style nuances.

## introduction many international enterprises operating in diverse markets worldwide translate their content into multiple languages and localize it to the specific variants spoken by their target audiences. despite the overarching goal of effective engagement, localization strategies can vary significantly between languages and even among different variants of the same language, due to factors such as translation volume, data availability, audience size and potential clients in each region, with the ultimate objective being to choose the most efficient and best suited solution for each market @xcite . in this paper, we investigate the extent to which localization strategies can be streamlined for different variants of the same language. we propose a standardized workflow based on a common, human reviewed neural machine translation (nmt) root, and a set of optional ai-powered post-editing steps that utilize large language models (llms), retrieval-augmented generation (rag), prompting techniques, and language resources. these steps are designed to address the divergences of each variant from the designated base and to make the necessary adjustments for adapting that base to different locales. ## experimental settings this study is based on proprietary bilingual datasets provided by a commercial client in the entertainment industry. the source language is english (en) -reflecting its centrality in both the client's operations and global markets-and the data comprise user interface segments and marketing copy. this content type is well-suited for the experiment because it constitutes a relatively low-risk domain, where minor regional inaccuracies are unlikely to cause significant consequences, and because it presents distinct localization challenges, as it often demands cultural specificity and audience engagement over linguistic neutrality. the same datasets are used throughout all stages of the experiment; they cannot be publicly released due to confidentiality agreements. we focus on spanish (es), a language with numerous regional variants spoken in strategically important markets. beyond its commercial relevance, es offers a compelling case for this study due to the diversity of its variants across lexical, grammatical, and stylistic dimensions, each potentially requiring different adaptation strategies. the study examines three specific locales: latin american (es-la), argentine (es-ar), and mexican spanish (es-mx). due to the involvement of professional human translators in both comparison and evaluation tasks, the datasets are relatively small: 1637 segments for @xmath0, 1635 for @xmath1, and 1624 for @xmath2. for cross-variant comparisons, only overlapping segments were retained: 1567 between es-la and es-ar, 1495 between es-ar and es-mx, and 1474 between es-la and es-mx. the es-la variant used is a commercially standardized form designed to be broadly accessible in pan-regional contexts when full regional localization is not feasible. the selection of only these three variants was guided by strategic relevance, data availability, and time constraints. while the approach is designed for potential reuse, its applicability to other locales and/or languages requires further specific testing and validation. it should also be noted that this approach is not intended for direct application to terminologyintensive domains such as legal or medical translation, which demand domain expertise, stricter quality controls, and accommodate more complex patterns of locale-specific terminology variation. all llm-based evaluations were conducted using openai's gpt models, selected for their accessible fine-tuning capabilities (openai). we chose gpt-4o mini due to its strong performance in automatic post-editing @xcite and its cost-efficiency relative to other openai models @xcite . while we hypothesize that similar outcomes could be achieved with alternative models from other providers with minimal prompt adjustments @xcite , model comparison was beyond the scope of this study, which focused on process development rather than tool benchmarking. prompting workflows were limited to three iterations per process and locale, following "green ai" principles @xcite . fine-tuning used no more than 55 examples per task. all experiments ran on 4 cpus (core i3-10350k) over 10h 30min, with an estimated carbon footprint of 276.37 gco 2 e (1.62 kwh), equivalent to 0.30 treemonths in spain (calculated using @xcite . ## the spanish variations to identify effective methods for transforming one es locale into another, we first needed a clear understanding of what those transformations would entail. to this end, we conducted a contrastive linguistic analysis @xcite on reference translations from en into es-la, es-mx and es-ar, all produced by the same nmt engine and post-edited by professional native linguists. this analysis led to the identification of the four categories of cross-locale divergences described below: while this categorization is based on linguistic criteria, its primary purpose is to group elements according to the similarity or compatibility of the rules governing their transformation, thus enabling a shared adaptation approach. ## adaptations after defining the baseline locale, we proceeded to develop an automatic post-editing method for each of the previously defined categories of differences. we adopted a segment-level approach, iterating through segment pairs to individually perform automatic post-editing on each of them. ## results to evaluate the results of our experiments, we compared them to the reference translations, also using bleu and levenshtein edit distance metrics, which let us assess the degree of improvement from the starting point (reflected in to gain a deeper understanding of the results, we asked es-ar and es-mx native linguists to review the segments where the adaptation output differed from the reference translation. they were asked to classify each sentence into one of three categories: acceptable differences (alternative translations that are equally appropriate for the locale and content type), minor errors (slightly inadequate but still intelligible or contextually plausible translations), and critical errors (unacceptable mistakes that compromise correctness or clarity in the given context). this additional review and categorization was necessary because the translation metrics used capture deviation from a reference, but do not account for the possibility of multiple valid renderings. therefore, a lower score does not necessarily indicate that a segment is incorrect or unsuitable. as figure 2 shows, from a sample of 1474 segments, out of the 262 es-la translations that initially differed from the es-mx reference, 17% (46) were perfectly adapted to match the es-mx translations, while 216 were not adapted to exactly match the reference. among those, only 3% (7) were identified as critical errors, 10% (26) as minor errors, and 70% (183) as not requiring further adaptation. as for es-ar, figure 3 shows that from a sample of 1567 segments, out of the 625 es-la translations that initially differed from the es-ar reference, 48% (299) were perfectly adapted to match the es-ar translations, while 326 were not adapted to match the reference. among those, 8% (49) were identified as critical errors, 7% (43) as minor errors, and 37% (234) as not requiring further adaptation. we also asked the linguists to classify the segments labeled as "errors", both critical and minimal, into the four categories defined in section 3: terminology, vocabulary, grammar and style. results for es-mx and es-ar can be found in figure 4 and figure 5 respectively, and they show that the objective stated in section 4.4 was achieved: most of the fixes translators would have to perform pertain to the output's style. in short, 87% of the es-mx and 85% of the es-ar automatically adapted segments would be ready for immediate publication, significantly reducing the amount of human post-editing effort involved. ## conclusions in conclusion, this paper has introduced an innovative approach to same-language localization by leveraging the contextual understanding and generative capabilities of llms, along with linguistic resources and prompting techniques, to re-imagine the task as more akin to a specific type of postediting rather than a completely separate process. this method provides a deeper understanding of translation and localization workflows, mitigating the need for developing and maintaining multiple localization strategies and translation models for the different locales of a language, and allowing us to understand the rich and complex relationships between them. the results demonstrate that this approach is feasible for marketing and product/ui content in spanish, both for variants that exhibit multiple types of divergences from the chosen baseline locale and for those presenting just one. while not perfect without subsequent human reviewing, these processes can significantly reduce the implicated human post-editing efforts in the more mechanical type of adjustments, allowing linguists and translators to concentrate almost exclusively on the more creative aspects of their work, mainly related to style and brand identity. future steps involve expanding the approach to more language pairs, particularly those comprising non-romance languages, which would present very different challenges. furthermore, final data collected through this process could be used to finetune an llm, exploring whether style adaptations -which we did not succeed in automating-can be taught to the llm through demonstration rather than explicit instructions. vera senderowicz. 2024. from "comment allez-vous?" to "comment ça va?": leveraging large language models to automate formality adaptation in translation. in proceedings of the 16th conference of the association for machine translation in the americas (volume 2: presentations), pages 237-254, chicago, usa. association for machine translation in the americas. satoru uchida. 2024. using early llms for corpus linguistics: examining chatgpt's potential and limitations. applied corpus linguistics, 4(1):100089. celia uguet, fred bane, mahmoud aymo, joão torres, anna zaretskaya, and tània blanch miró blanch miró. 2024. llms in post-translation workflows: comparing performance in post-editing and error analysis. in proceedings of the 25th annual conference of the european association for machine translation (volume 1), pages 373-386, sheffield, uk. european association for machine translation (eamt). sara zanzottera. 2024. controlling llm translations of invariant elements with rag. https://drive.google.com/file/d/ 1bvcnbsagwp25edpiq5ljyos3_eneo3wu/view?@xmath3. accessed: 2025-02-07. a appendix a. example prompt to generate terminology adaptations when an equivalent term in es-la is available you are a spanish-speaking linguist from mexico/argentina. you are instructed to: 1. read the original english text: ′ originaltext ′ . can you find any en terms from the translation guide below in it? if you can't, stop reading the instructions and don't do anything else. if you do, go on to step 2 below. 2. read the spanish translation: ′ spanishtranslation ′ . if ′ enterm ′ is translated as ′ eslaterm ′ in the spanish translation, replace it for ′ esm xterm ′ | ′ esarterm ′ . the replacement should be case-insensitive but should respect the original capitalization of the term in the text. follow these general instructions: a. watch out! don't do a "search and replace" type of job. the terms from the translation guide might have a different gender, number or capitalization in the text and still be the same. example 1: if the translation guide includes the term "phones", and you find "phone" in the english text, you can consider them a match. example 2: if the translation guide includes the term "callejón" and you find "callejones" in the spanish translation, you can consider them a match, even if the word is in plural and capitalized. be smart about that when you're editing. b. morphology matters a lot in spanish: when you replace spanish word for another, make sure all articles and adjectives related are adapted accordingly. don't produce outputs like "el chica" or "las guapos altas", which are agrammatical in spanish. the same goes for verbs: when you do replacements, make sure the original conjugation from the text in spanish is respected. c. after applying only those changes, return the final version of the translation, without any extra words, explanations, or headers. translation guide: en term -> esla term -> esmx term | esar term b appendix b. example prompt to generate terminology adaptations when no equivalent term in es-la is available you are a spanish-speaking linguist from mexico/argentina. you are instructed to: 1. read the original english text: ′ originaltext ′ . can you find any en terms from the translation guide below in it? if you can't, stop reading the instructions and don't do anything else. if you do, go on to step 2 below. 2. read the spanish translation: ′ spanishtranslation ′ . make sure ′ enterm ′ is translated as ′ esm xterm ′ | ′ esarterm ′ in the spanish text, and make necessary adjustments if it's not. follow these general instructions: a. watch out! don't do a "search and replace" type of job. the terms from the translation guide might have a different gender, number or capitalization in the text and still be the same. example 1: if the translation guide includes the term 'phones', and you find "phone" in the english text, you can consider them a match. example 2: if the translation guide includes the term "callejón" and you find "callejones" in the spanish translation, you can consider them a match, even if the word is in plural and capitalized. be smart about that when you're editing. b. morphology matters a lot in spanish: when you replace spanish word for another, make sure all articles and adjectives related are adapted accordingly. don't produce outputs like "el chica" or "las guapos altas", which are agrammatical in spanish. the same goes for verbs: when you do replacements, make sure the original conjugation from the text in spanish is respected. c. after applying only those changes, return the final version of the translation, without any extra words, explanations, or headers. translation guide: en term -> no-term -> esmx term | esar term c appendix c. example prompt to generate terminology adaptations when the translation guide includes more than one term you are a spanish-speaking linguist from mexico/argentina. you are instructed to: 1. read the original english text: ′ originaltext ′ . can you find any en terms from the translation guide below in it? if you can't, stop reading the instructions and don't do anything else. if you do, go on to step 2 below. make sure that every en term is translated as its corresponding esmx term in the spanish translation, and not as its esla term. make the necessary replacements to make that true. the replacement should be case-insensitive but should respect the original capitalization of the term in the text. follow these general instructions: a. watch out! don't do a "search and replace" type of job. the terms from the translation guide might have a different gender, number or capitalization in the text and still be the same. example 1: if the translation guide includes the term "phones", and you find "phone" in the english text, you can consider them a match. example 2: if the translation guide includes the term "callejón" and you find "callejones" in the spanish translation, you can consider them a match, even if the word is in plural and capitalized. be smart about that when you're editing. b. morphology matters a lot in spanish: when you replace spanish word for another, make sure all articles and adjectives related are adapted accordingly. don't produce outputs like "el chica" or "las guapos altas", which are agrammatical in spanish. the same goes for verbs: when you do replacements, make sure the original conjugation from the text in spanish is respected. c. after applying only those changes, return the final version of the translation, without any extra words, explanations, or headers. translation guide: en term -> no-term -> esmx term | esar term en term -> esla term -> esmx term | esar term en term -> no-term -> esmx term | esar term