From 'LLMs Struggle' to 'LLMs Excel': Claude Sonnet 4 + XLIFF 2.1

Published: 2025-01-10 | Author: Transl8ly Team

claude xliff translation machine translation llm accuracy quality

The Rise of the LLMs: GPT and Translation

Large Language Models (LLMs) like OpenAI's GPT series (the power behind ChatGPT) have demonstrated incredible capabilities in understanding and generating human-like text. It's natural to assume they would also be excellent translators. Indeed, you can ask ChatGPT to translate text, and it often provides a reasonable result.

However, when it comes to the specific, demanding task of high-quality translation, especially for professional use cases like translating InDesign documents, general-purpose LLMs often fall short compared to specialized translation engines.

Why General-Purpose GPT Models Can Falter in Translation

GPT models are designed for a broad range of language tasks: summarization, question answering, creative writing, coding, and more. Translation is just one of many functions they are trained on.

This broad focus can lead to weaknesses in translation:

  1. Lack of Specialization: The model's architecture and training data are not solely optimized for the nuances of translation between specific language pairs. Dedicated MT engines train vast neural networks specifically on parallel corpora (aligned translated texts) to master this task.
  2. Consistency Issues: LLMs can sometimes be inconsistent in their terminology or phrasing within the same document, especially longer ones. They might translate the same term differently depending on the immediate context provided in the prompt.
  3. Hallucinations/Creativity: While amazing for creative tasks, this ability can be detrimental to translation. LLMs might occasionally "invent" information or interpretations not present in the source text, prioritizing fluency over strict fidelity.
  4. Nuance and Idiom Handling: While improving, GPT models may still mistranslate subtle cultural nuances or idiomatic expressions that specialized engines are specifically trained to handle.
  5. Latency and Cost: Getting translations via API calls to large, general-purpose LLMs can sometimes be slower and potentially more expensive than using optimized, dedicated translation APIs.

Why Structure Matters: XLIFF 2.1 + Large Context

By converting IDML to XLIFF 2.1 with placeholders, grouping, and contextual notes, we give the translator model a clean, context‑rich view of exactly what to translate—no more, no less. Our pipeline preserves placeholders, numbers, and whitespace and verifies everything on return.

Transl8ly + Claude Sonnet 4 = Expert InDesign Translation

Today we pair that advanced XLIFF 2.1 workflow with Claude Sonnet 4’s large context window and strong instruction following. The result:

  • High fidelity: Placeholders, numerals, and formatting preserved.
  • Better context: Grouping and notes feed section‑level meaning.
  • Lower QA time: Fewer errors to fix in layout.

Conclusion: The Right Tool for the Job

LLMs have evolved fast. With the right structure (XLIFF 2.1), guardrails, and validators, Claude Sonnet 4 delivers professional‑quality translations in IDML workflows—saving hours while maintaining design integrity.