Break-Ideate-Generate ( B r I d G e): Moving beyond Translations for Localization using LLM s (2025)

Abstract: Language localization is the adaptation of written content to different linguistic and cultural contexts. Ability to localize written content is crucial for global businesses to provide consistent and reliable customer experience across diverse markets. Traditional methods have approached localization as an application of machine translation (MT), but localization requires more than linguistic conversion – content needs to align with the target audience’s cultural norms, linguistic nuances, and technical requirements. This difference is prominent for long-form text, where multiple facts are present in a creative choice of language. We propose a novel prompt approach for Large Languages Models (LLMs), called Break-Ideate-Generate (BrIdGe), for language localization. BrIdGe ‘breaks’ the source content into granular facts, ‘ideates’ an action plan for content creation in the target language by organizing the granular facts, and finally executes the plan to ‘generate’ localized content. This approach emulates the cognitive processes humans employ in writing that begin with identifying important points, followed by brainstorming on how to structure and organize the output. We evaluated the BrIdGe methodology from multiple perspectives, including impact of BrIdGe prompt on different LLMs and performance comparisons with traditional MT models and direct translation through LLMs on public benchmark and proprietary e-commerce datasets. Through human and LLM-based automated evaluations across content in multiple languages, we demonstrate effectiveness of BrIdGe in generating fluent localized content while preserving factual consistency between source and target languages.

## introduction with the globalization of businesses and the need to cater to diverse audiences worldwide, content local-* these authors contributed equally to this work ization has become crucial @xcite . localization adapts content originally designed for a source region to meet the cultural, linguistic, and technical requirements of different target regions @xcite . for businesses with diverse customer bases, effective localization is paramount to create accessible experiences for customers, regardless of their location, language, or cultural background. specifically, for written content, localization goes beyond translation, as the latter only focuses on linguistic conversion keeping same structure and stylistic expressions from source to target language @xcite . whereas content localization allows modification in content structure, idiomatic expressions, and information organization to ensure native-like fluency while preserving factual alignment. for instance, the english idiom "boat neck dresses can be dressed up or down easily" imply that the dress can be used for both formal and casual occasions. however, machine translation (mt) tools like aws translate foot_0 and google translate foot_1 , translate this idiom to portuguese as "este vestido pode ser facilmente vestido para cima ou para baixo" which is an incorrect literal translation meaning boat neck dresses can be worn on top as well as on bottom. figure 1 shows nuances of localization which are missed by translation. large language models (llms) pre-trained on large text corpus @xcite @xcite have demonstrated exceptional abilities to abstract the factual knowledge in their weights @xcite , follow instructions and perform chain-of-thought (cot) reasoning @xcite . this has enabled them to break down complex problems into smaller, more manageable steps, mirroring human cognitive processes. llms have also showed impressive multilingual capabilities with promising results on numerous multi-lingual natural language processing (nlp) tasks @xcite @xcite . in this work, we leverage llms to emulate the human writing behavior @xcite , where we first note down our initial and granular thoughts, followed by contextually structuring the information as per the requirements of final use-case. and we demonstrate its efficacy for the task of textual content localization from a source language to a target language. to achieve this, we propose a novel prompting approach called break-ideate-generate (bridge) for llms. given content in a source language, bridge first 'breaks' it into granular facts, then 'ideates' an execution plan and finally 'generates' content in the target language. we perform extensive experiments on public benchmark datasets for multiple languages pairs and demonstrate superior performance of the bridge prompt in comparison to standard translation prompts for multiple llms. we also show effectiveness of bridge in a real-world e-commerce application of localizing educational content. in this application, we generate educational content about product attributes and benefits with the objective of aiding customers in taking informed shopping decisions. for example, given a chair with an attribute "finish type" as "lacquer", we generate content around properties and benefits of chairs with lacquer finish. here the original content is generated in english language and the task is to localize it to languages of non-english-speaking marketplaces. manual audit by language and marketplace experts demonstrates that bridge outperforms state-of-the-art translation strategies on fluency, while maintaining factual consistency between source and target languages. the major contributions of this paper are: ## related works with the rise of internet and social media, the need for effective language localization has become increasingly important. traditionally, human translation was the primary approach for localization, with professional translators adapting content to suit different linguistic and cultural contexts. however, human translation is time-consuming and expensive. with machine learning, statistical @xcite and neural @xcite mts became dominant approaches. while mt has shown significant improvements in recent years, it still faces challenges in terms of accuracy and fluency @xcite . also, its performance in translating cultural-specific items remains poor due to the gap between the cultural differences associated with languages @xcite . in this paper, we combine various lines of research on multi-lingual llms and its reasoning capabilities to localize content. our approach primarily comprises of the following steps. the first step is named break, which segments the original content into granular facts. this strategy is being widely adopted for hallucination detection and correction @xcite @xcite . to the best of our knowledge, this is the first work which adopts the strategy of breaking content into granular facts for localization. llms have demonstrated improved performance in analytical tasks achieved by encouraging them to generate internal thoughts or logical chains before responding @xcite , and allowing them to update their initial responses through self-critique @xcite . this strategy is named chain-of-thought (cot). we leverage these techniques in the bridge prompt, to execute all the instructions in the prompt stepby-step generating outputs at intermediate step conditioning the final generated localized content to effectively adhere all the steps. ## bridge: break-ideate-generate in this section, we describe our approach to localize content, which assumes access to an llm that can be prompted, and content generation in both source and target language. another key assumption of our method is that this language model, when suitably prompted, can both create and execute a plan to generate responses adhering to specific criterion and instructions. we introduce bridge, a novel method for content localization inspired by human writing behavior. our approach is illustrated in figure 2 . bridge prompt first instructs llm to break input content into granular facts (section 3.1), next to ideate content generation plan appropriate for the specified marketplace and use-case (section 3.2), and finally execute the plan to generate the target language content by organizing the granular facts (section 3.2). while there are multiple steps in our workflow, we created a unified prompt, which can perform these steps and generating the localized content in a single llm call. ## conclusions & future work this paper introduced bridge, a novel prompt strategy for performing comprehensive content localization beyond linguistic translation. by emulating human writing through iterative steps of breaking down input, ideating a localization plan, and generating the final output, bridge demonstrates promising localization of content by preserving meaning and achieving fluency. experiments in four languages for educational content showed the strengths of bridge. it achieved comparative adequacy scores to baselines while outperforming them with fluency. qualitatively, bridge preserved meaning across long and complex sentences, appropriately handling domain-specific context. most importantly, we observed the impact of the break step, going beyond the standard chain-of-thought strategy, by segmenting input facts, which allows flexibility to the llm to organize and reconstruct the final output generating fluent content.going forward, we plan to experiment this framework with moderate to small sized llms to optimize the cost and latency constraints that come with large llms like claude. we also plan to experiment our approach to more indigenous languages and using low resources languages as the source one.