Modern machine translation systems are developed around two key technologies: neural machine translation (NMT) and large language models (LLM). Although both are based on the Transformer architecture. They work differently and have different advantages. Understanding how these approaches differ and which tasks each is more effective at is becoming critically important for companies and professionals working with translations. In this article, we will look at the features of NMT and LLM, their advantages, limitations, and analyze which solution is better suited for different types of tasks.
What Is Neural Machine Translation (NMT)?
Neural Machine Translation (NMT) is an approach to translation based on deep neural network models that analyze the entire sentence rather than individual words. Today, virtually all industrial NMT is built on the Transformer architecture, where the encoder and decoder work through multi-level self-attention and cross-attention mechanisms. This allows the model to effectively capture meaning, syntax, and context, ensuring high translation accuracy and stability
Unlike previous statistical and recurrent methods, Transformer-NMT provides a deeper understanding of sentence structure and better handles long texts, complex constructions, and rare vocabulary. The models are easily adaptable to specific domains through fine-tuning, domain tags, or lightweight layers (e.g., LoRA), making them particularly effective for legal, medical, and technical fields where terminology and strict accuracy are important.
What Are Large Language Models (LLMs)?
Large language models (LLMs) are neural network systems based on the Transformer architecture and trained on large-scale text corpora. Their key feature is the ability to process context as a whole and model word sequence probabilities, which allows them to understand queries and generate coherent text.
During operation, LLMs convert text into internal representations using multi-layer self-attention, which allows the model to take into account the relationships between all parts of the text simultaneously. This provides flexibility and the ability to process complex phrases, long dependencies, and ambiguous wording. The generation mechanism is based on predicting the next token, which allows you to form a coherent response or continuation of the text step by step.
Differences Between NMT and LLM-Based Translation
Neural machine translation (NMT) is specifically designed to translate texts between languages. It is trained on parallel corpora – large collections of texts where each sentence in the source language has an exact translation in the target language. Therefore, NMT performs well with direct translations, especially when the text is formal or technical, and the sentence structure in the source and target languages is similar. For example, NMT will translate standard legal or scientific text accurately and literally.
Large language models (LLMs) are trained on large volumes of diverse text rather than parallel translation data. In translation, they rely on general linguistic knowledge and the ability to take into account a broad context, which allows them to better understand the structure of a phrase and its semantic connections.
Unlike NMT, which is optimized specifically for direct correspondence between two languages, LLMs select translations based on probabilistic language models. Therefore, LLMs often produce more natural phrasing in stylistically rich or expressive texts.
Key Advantages of NMT and LLMs
Advantages of Neural Machine Translation
Modern machine translation systems based on Transformer architecture provide high accuracy and stability thanks to the effective use of self-attention and cross-attention mechanisms. Such models preserve the structure of the source sentence well, correctly process long dependencies, and give predictable results. This is especially important for legal, technical, and other formal texts that require accurate transmission of terms and minimal variability.
NMT is also well suited for mass translation. The models work quickly, allow large amounts of data to be processed at low cost, and maintain consistent quality when scaled. Thanks to the possibilities of retraining, adapters, and domain tags, they are easy to adapt to a specific subject area, which helps to achieve high accuracy even in highly specialized fields.
Another important advantage is the ability to control the translation process. Glossaries, constrained decoding, and other tools can be used to manage terminology, style, and text format. This predictability makes NMT a reliable foundation for industrial localization processes, where consistent quality and compliance with industry requirements are particularly important.
Advantages of Large Language Models
LLMs have a wide range of language data and are capable of working flexibly with syntax and semantic relationships. In translation tasks, they perform well with literary texts, dialogues, and colloquial constructions, where it is important to convey the overall style and naturalness of speech. LLMs are better at interpreting complex, unstructured, or multi-layered sentences, ensuring coherent and fluent text.
The models can perform additional operations that go beyond direct translation: rephrasing, simplifying text, checking semantic consistency, or eliminating ambiguities. This makes LLMs useful in post-editing chains and in scenarios where stylistic alignment or readability improvement is required.
LLM customization is primarily style-oriented: the model can be adapted to an artistic, conversational, or brand tone. This adjustment helps tailor the translation to the desired format or audience, although precise terminology control is less effective than with NMT.
Key Limitations of NMT and LLM
Limitations of NMT
NMT relies heavily on parallel data. For domains with limited or highly specialized corpora, the model may struggle with rare terms and specific phraseology. This is particularly noticeable in domains where content is rapidly updated or includes unique terminology.
Despite advances in fine-tuning and adapter methods, high-quality domain adaptation requires carefully prepared data and time. Switching quickly between multiple domains or projects remains more difficult than with more flexible LLMs.
NMT focuses on literal matching and strictly follows the structure of the source text, which limits its use for stylistically free materials. In the absence of domain data, the system may misinterpret idioms, cultural references, or colloquial expressions. Keeping terminology up to date requires updates to dictionaries and datasets.
Limitations of LLMs
LLMs tend to interpret text: they can change sentence structure, rephrase meaning, or generalize technical details. This makes them less reliable for tasks where strict accuracy and minimal variation are important, especially in legal, medical, and technical translations.
Another significant limitation is possible “hallucinations.” When unfamiliar with a term or object, the model may select the most statistically probable word rather than the correct term, which creates risks for specialized fields where accuracy is critical.
LLMs have higher computational costs and latency compared to NMT, especially when processing long documents. This makes them economically inefficient for mass translations or high-speed scenarios.
In addition, LLM models are non-deterministic: the same input can produce different translation options. This complicates quality control, terminology consistency, and the application of strict guidelines. Additional infrastructure is required to achieve stability, such as retrieval systems, validation, or rules.
Hybrid Approaches and Industry Trends
Hybrid MT approaches combine the strengths of NMT and LLMs to achieve both accuracy and linguistic flexibility. One common method is using LLMs to support NMT training: LLMs can generate synthetic parallel data, help refine domain-specific terminology, or improve alignment quality. This allows NMT systems to benefit from broader linguistic patterns while maintaining the efficiency and determinism of traditional engines.
In production workflows, LLMs are often applied for post-editing. After NMT generates a baseline translation, an LLM can adjust style, resolve ambiguous segments, or improve overall fluency. This reduces the amount of manual editing required and provides more natural output without compromising the precision ensured by NMT.
Retrieval-augmented setups are also gaining traction. By integrating translation memories, terminology databases, or domain-specific corpora, both NMT and LLM pipelines can enforce consistent wording and reduce the risk of hallucinations or terminology drift. This approach enhances reliability in regulated domains where strict adherence to vocabulary and structure is essential.
Hybrid solutions are already widely used across industries. Organizations rely on NMT for consistent, large-scale translation and incorporate LLMs where deeper interpretation or stylistic adaptation is needed. This combination provides a practical balance between accuracy, cost efficiency, and linguistic flexibility.
When Businesses Should Use NMT vs. LLM
Use NMT when:
- A strict, accurate, and reproducible translation without stylistic deviations is required.
- Documents contain terminology, formal constructions, or regulated vocabulary (legal, medical, technical texts).
- Predictability and consistency are important: same input – same output.
- You need to process large volumes of text with consistent quality and low cost.
- Strict control of terminology and style is required through glossaries, domain rules, and constraints.
Use LLM when:
- You need to preserve the style, expressiveness, or tone of the text (marketing, storytelling, dialogues).
- The content contains complex, unstructured, or ambiguous phrases that require interpretation.
- You need to rephrase, simplify, improve readability, or stylistically align the translation.
- You need flexible adaptation to colloquial, artistic, or corporate style.
- You need additional features beyond translation: summarization, editing, or semantic verification
In practice, many businesses achieve the best results using hybrid workflows: NMT for core translation and LLMs for refinement, stylistic alignment, and resolving ambiguity – combining precision with expressiveness.
