Why GPT Models Struggle with Translation (and Why DeepL Excels)

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 DeepL is Different: A Focus on Translation Excellence

DeepL, in contrast, was built from the ground up with one primary goal: to be the best machine translation engine possible.

• Specialized Architecture: DeepL employs unique neural network architectures and training techniques specifically designed for translation tasks.
• Targeted Training Data: It's trained extensively on massive, high-quality parallel texts curated for translation accuracy across its supported language pairs.
• Emphasis on Nuance: The engine is renowned for its ability to capture context and produce natural-sounding, nuanced translations, particularly between European languages.
• Consistency: DeepL generally exhibits strong consistency in terminology and phrasing.
• Efficiency: Its infrastructure is optimized for delivering fast and cost-effective translations via its API.

Transl8ly + DeepL = Optimal InDesign Translation

Transl8ly integrates DeepL specifically because of its recognized superiority for translation tasks. When you translate an InDesign IDML file with Transl8ly:

• You benefit from DeepL's specialized translation quality, resulting in more accurate and natural text.
• This higher initial quality reduces the time and effort needed for post-editing in InDesign.
• You get the speed and cost-effectiveness of a dedicated MT service combined with Transl8ly's layout preservation.

Conclusion: The Right Tool for the Job

While GPT models are powerful tools for many language tasks, they are not specialized translation engines. For professional translation workflows, especially when dealing with design files where accuracy, nuance, and consistency are paramount, a dedicated engine like DeepL provides measurably better results. Choosing Transl8ly ensures you leverage this best-in-class translation technology for your InDesign projects, leading to a more efficient workflow and a higher-quality final product.