Enterprise Automated Translations: LLM or Neural MT?

Researched and written by Rakshitha Thirumalaivasan and Laszlo K. Varga.

The quandary

In the realm of machine translation (MT), the evolution from traditional neural machine translation (NMT) – pioneered by Google Translate in 2016 – to the advent of large language models (LLM) marks a significant paradigm shift. The introduction of LLMs, such as GPT-4 and its counterparts, has ignited discussions within the industry about the potential transformation of automated translations.

Despite the wealth of conflicting opinions on whether, when, and how LLMs will supersede NMT models, concrete data is scarce to support answers to these questions.

With the proliferation of platforms and engines, the choices are complex and tangled. How relevant is the emergence of general-purpose LLMs steered by prompt engineering in automated translations, compared to conventional NMT models and purpose-trained multilingual LLMs?

Let’s look into it.

This has been a preview. The full report can be accessed online by Nimdzi Partners.

The full publication available to Nimdzi Partners explores the main aspects to be considered for internationalization along with best tools and best practices for internationalization.

The article was researched and written by Nimdzi's Technology Researcher Rakshitha Thirumalaivasan.

The article was researched and written by Nimdzi's Lead Researcher and Analyst, Laszlo K. Varga.

3 April 2024
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