How Useful Are Large Language Models (LLMs) For Localization?

Large Language Models, often abbreviated to LLM, are a type of artificial intelligence (AI) increasingly used in localization for automated translation and localization tasks, natural language processing and content generation.

The recent emergence of LLMs like ChatGPT represents a transformative development in artificial intelligence, revolutionising human-computer interaction and automating many tasks currently performed by humans. 

The ability of LLMs to learn from vast data sets and respond to natural language inputs with human-sounding responses has far-reaching implications for technology, society, and multilingual communication. 

LLMs can facilitate text and voice translation between almost any language, breaking down linguistic barriers, fostering collaboration and understanding among people from diverse cultural backgrounds. 

The translation and automation capabilities of LLMs are being widely leveraged within the localization industry. 

The Use of Large Language Models (LLMs) in Localization

The use of LLMs within localization (l10n) can be summarised across three key areas:  

  1. Translation & Localization: LLMs can automate translation and localization tasks, providing quick translations of text with reliable levels of accuracy and helping to expedite the translation process for software UI elements, content, and documentation.

    LLMs can also assist in generating localized variants of text, adapting content to the cultural and linguistic nuances of different target markets.
  2. Natural Language Processing: LLMs excel at understanding and generating natural language, which is crucial for handling user input, processing multilingual data, and supporting language-specific features.

    LLMs can also parse user queries and extract information, responding in the appropriate language or dialect.
  3. Content Generation: LLMs can generate multilingual content, such as dynamic user notifications, localized marketing materials, or personalized messages.

    They can also adapt content based on user preferences, language settings, or geographic location, enhancing the personalized experience for international users.

The challenges of using LLMs for localization 

Though LLMs have revolutionized the localization industry in a short space of time, they aren’t a silver bullet for automated translation, with their use posing a number of challenges to the L10n process:

  • Accuracy & Quality: While LLMs provide impressive language capabilities, they are not perfect and can occasionally produce incorrect or awkward translations.

    Maintaining high translation accuracy and quality is crucial in the internationalization process (a subset of localization), so careful validation and review are necessary to ensure the generated translations are accurate, culturally appropriate, and contextually relevant.
  • Domain & Industry Specifics: LLMs like ChatGPT are trained on general datasets (drawn from the internet), so may lack domain-specific knowledge or terminology.  This limitation can impact the accuracy and appropriateness of translations within specialized industries or technical content. 

For L10n/i18n projects requiring industry-specific terminology, additional data training or customization of LLMs may be necessary, which requires technical expertise and an appreciation of the privacy concerns regarding the datasets used.

  • Context & Cultural Sensitivity: LLMs may not fully grasp the cultural context or sensitivity of certain language constructs, resulting in potential inaccuracies or unintentional biases in translations. Such errors can lead to reputational damage or legal issues.
  • Security: As prompts, outputs and training data may contain sensitive information, LLMs’ security is paramount. There is also the risk of the malicious skewing of data, aka ‘poisoning’ to influence outputs.

Incorporating LLMs in the localization process

Given the limitations of LLM-based translation, they cannot completely replace humans within the localization process but should instead be incorporated with L10n and i18n to maximise the efficiencies they offer in a controlled manner that mitigates potential errors.

  • Human Review & Editing: A Human review process should validate and refine translations generated by LLMs.

    Linguistic experts or professional translators can ensure accuracy, cultural appropriateness, and quality in localized content. Their expertise can help resolve nuanced linguistic challenges and maintain consistency.
  • Customization & Fine-Tuning: LLMs can be trained and fine-tuned on relevant industry-specific terminology, content and  domain-specific data to enhance their performance in specialized areas.

    LLM customisation can provide more accurate and contextually appropriate translations for specific domains
  • Iterative Refinement: Incorporating user feedback and continuous improvement cycles can refine translations over time. This helps identify and address recurring issues, improve accuracy, and adapt the translations based on user preferences and regional variations.

    Improved and refined translations can be fed back into LLM training sets to create a continually improving output based on the specific context and audience.
  • Data Handling: Before sensitive data or information is fed to LLMs, it should go through a human-led anonymisation process. The implications of using LLMs for localization should be included within existing data handling practices to ensure compliance and security.

Practical applications of LLMs within localization

Machine translation, such as Google Translate, has enabled automated translation for almost 20 years, but LLMs are enabling a much broader range of practical applications within localization.

Multilingual Chatbots – LLMs like ChatGPT support 50 languages and can communicate automatically with customers through advanced AI chatbots using services like Botpress

Multilingual chatbots open up new commercial markets at a fraction of the cost of employing humans for customer service or acting as virtual sales assistants.

Automated translation –  LLMs enable automated content translation, delivered via API, across a wide range of formats and contexts:

  • Website content
  • Real-time messaging
  • Video narration, subtitles & audio description
  • Image PDFs, scanned documents, invoices and receipts

Shopify, an e-commerce platform, utilizes LLMs to automate the translation of product information, marketing content, and user interface elements (internationalization) into various languages.

This enables Shopify merchants to sell their products in different regions with localized content and interfaces.

➡️ Find out how Lingoport’s product suite can automate localization tasks to reach new markets – Learn More

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