Machine translation and large language models: understanding the difference

Nov 07, 2025

Artificial intelligence is changing how multilingual content is produced across all industries, including the life sciences. But while the terms machine translation (MT) and large language models (LLMs) are often used interchangeably, they refer to very different technologies. Understanding how they work and where each excels helps companies make informed decisions when managing sensitive, high-stakes content such as clinical documentation, device labeling, or patient communication.

How traditional machine translation works

Machine translation as we know it today is based on neural networks. Neural Machine Translation (NMT) systems, such as Google Translate or DeepL, are trained specifically to translate text from one language to another. They rely on large sets of parallel bilingual data, millions of paired sentences in both languages, to learn statistical patterns that help predict how a sentence in one language should appear in another.

These models are task-specific: they are built and optimised for translation. When the input sentence follows predictable grammar and terminology, MT performs well, offering speed and consistency. For repetitive and technical content such as instructions for use, regulatory templates, or clinical data fields, NMT can achieve high accuracy, especially when fine-tuned on domain-specific data.

However, MT systems also have clear limitations. They are less capable of handling:
• ambiguity or creative language
• long-range dependencies across sentences
• context beyond the immediate segment being translated

Because they process text sentence by sentence, they can miss contextual nuances or fail to adapt tone and style appropriately, aspects that matter greatly in patient-facing or marketing content.

How large language models (LLMs) work

Large Language Models, like GPT or Claude, take a fundamentally different approach. They are general-purpose systems trained not just for translation, but to predict the next word in any type of text. They are exposed to massive amounts of multilingual data, books, articles, websites, and conversations, across many languages and topics. This broader training allows them to learn patterns of meaning, tone, and style far beyond direct bilingual equivalence.

Unlike MT, LLMs can consider the entire context of a paragraph or document. They understand relationships between sentences and can adapt the tone to different audiences. This gives them an advantage in producing fluent, natural, and stylistically consistent text. They also tend to handle idioms and complex sentence structures better than standard MT models.

However, LLMs are not specialised translation engines. Their output can be creative or interpretative, which means factual or regulatory accuracy is not guaranteed. They may also introduce hallucinations, plausible sounding but incorrect content, a serious risk in scientific or medical contexts.

In other words, where MT focuses on accuracy and consistency, LLMs excel at fluidity and contextual adaptation. Both strengths are valuable, but neither system is perfect on its own.

MT vs LLM: a side-by-side view

Feature	Machine Translation (NMT)	Large Language Model (LLM)
Purpose	Built specifically for translation tasks	Designed for multiple tasks including translation
Training data	Bilingual corpora (parallel texts)	Multilingual and monolingual data across domains
Focus	Segment-level accuracy and terminology consistency	Context, fluency, and natural language understanding
Strengths	Fast, consistent, predictable	Context-aware, adaptive, stylistically refined
Limitations	Literal, context-limited, rigid	May invent information or alter meaning

This comparison shows why the best translation strategies combine both technologies rather than choosing one over the other.

Performance varies by language and document type

In life sciences, translation challenges differ significantly between content types. A device manual or batch record requires strict terminology control and format consistency, which favors NMT. In contrast, patient-facing materials, such as educational leaflets or app interfaces, benefit from LLMs’ ability to understand tone, context, and emotional nuance.

Performance also varies by language pair. For widely spoken languages with abundant training data, NMT often produces strong results. For less common languages or specialised domains, LLMs may outperform MT by leveraging broader contextual understanding rather than relying solely on direct bilingual examples.

That is why the choice between MT and LLM should not be theoretical, but empirical, based on testing and comparison for each project type.

At Novalins, both technologies work hand in hand

At Novalins, we use both Machine Translation and Large Language Models as part of our AI-enhanced translation workflows, always under the supervision of life science experts. Before implementing a workflow, we test both MT and LLM outputs on a representative sample of the client’s content to evaluate which model performs best.

Performance criteria include:
• terminology accuracy
• sentence fluency
• consistency across segments
• regulatory compliance
• cultural and stylistic appropriateness

In many cases, MT remains the best fit for highly technical or repetitive text. In others, LLMs deliver superior readability and contextual accuracy. By testing both, we ensure the right balance between cost, speed, and quality for each specific project.

Crucially, all AI-assisted outputs are reviewed and validated by medical translators and subject-matter experts. This human oversight guarantees that the final text is accurate, compliant, and aligned with the client’s quality standards.

The future of translation in life sciences

As AI continues to evolve, the boundary between MT and LLMs is gradually blurring. Some modern systems already integrate both approaches, using LLMs to improve MT fluency or correct stylistic inconsistencies.

Still, technology alone cannot ensure compliance or patient safety. Translation in the life sciences will always require the judgment, ethics, and contextual understanding that only human experts provide.

At Novalins, we see AI not as a replacement, but as a precision tool, one that, when combined with human expertise, delivers optimal results for multilingual communication in healthcare and life sciences.

Curious to see whether MT or LLM performs best for your multilingual projects?
Try a free pilot with Novalins to compare both technologies on your content and discover which approach offers the best balance between accuracy, fluency, and compliance.