Machine Translation vs. AI Translation: What is the difference?

Modern automated translation technologies are developing rapidly, and two main approaches have come to the fore today: machine translation and translation using artificial intelligence.

What do these concepts mean exactly?

Machine Translation today refers to Neural Machine Translation (NMT) systems. These are programs that can be run either online or offline, where the user uploads text in the original language and receives a translated text.

AI translation refers to the use of broad-profile Large Language Models (LLMs). Most often, these models are represented in the form of chatbots: GPT, DeepSeek and others. Using these models, the translation process involves sending a chatbot a text translation task using a specially composed request, or prompt.

Confusion in distinguishing between these concepts may arise due to the fact that the structure of NMT and LLM systems is largely similar. Both technologies are based on neural networks, and both use transformer architecture. However, the difference lies in how the technologies are applied in the program. While NMT systems translate text by selecting equivalents in the target language for words or phrases from the original text, LLM systems regenerate a complete text with the same meaning in a foreign language.

The difference in how the programs work also determines the difference in translation style.

Syntax. NMT systems almost always preserve the syntactic structure of the source sentence when translating. The exception is the translation of clichés, if the NMT system has been trained in advance to translate them correctly. LLM systems, on the other hand, will tend to change the syntactic structure. At the same time, LLM tends to combine two sentences into one when translating, which must be taken into account in the prompt, since most CAT tools (e.g. memoQ, Trados) do not support mismatches in the number of segments in the original and translated texts. In the example below, you can see that the syntax of an NMT translation repeats the syntax of the original, while the LLM system translated it syntactically closer to the reference.

Source	Reference	LLM Translation (AI)	NMT Translation
С сентября 2023 года в России идет эксперимент по партнерскому финансированию (ЭПФ), который продлится еще один год.	Since September 2023, an experiment in partnership financing (PF) is underway in Russia, which stands to extend into next year.	Since September 2023, an experiment on partnership financing (EPF) has been underway in Russia, which will last for another year.	Since September 2023, Russia has been conducting an experiment on partnership financing (EPF), which will last for another year.

Naturalness. Text translated by an LLM system sounds more natural than translations produced by NMT systems. However, the naturalness of LLM translations tends toward neutrality in style and register, which can negatively affect the quality of certain translation types. For example, for technical texts where a specific word order, highly specialized abbreviations, etc. are important, machine translation is more suitable.

Source	Reference	LLM Translation (AI)	NMT Translation
Figure 2: Bakyrchik Zone 1 Underground Reticulation Pipeline Route Profiles	Рисунок 2. Рудник Бакырчик. Зона 1. Профили трасс подземных закладочных трубопроводов	Рисунок 2: Маршрутные профили подземного трубопровода в зоне 1 Бакырчик	Рисунок 2. Рудник Бакырчик. Зона 1. Профили трасс подземных сетчатых трубопроводов

Accuracy. NMT systems select translation equivalents, so they always preserve factual information. If the program does not have sufficient information to translate a word or phrase, it treats it as a proper noun and transliterates it. LLM systems regenerate text in the target language while preserving the meaning, which means that some extremely rare cases of “hallucinations” or LLM bias are possible during translation.

Hallucinations occur because the LLM system may “fill in the gaps” when it lacks the information necessary to respond. For example, if the LLM system does not know how to translate a term, it will “intuitively” translate it, and most often, the translation will be incorrect. Unlike the transliteration of NMT systems, these translations can be hard to find when post-editing the translation, as they may look quite plausible.

Source	Reference	LLM Translation (AI)
The other ingredients are mannitol, methionine, poloxamer 188, diluted phosphoric acid, sodium hydroxide, water for injections.	Les autres composants sont: mannitol, méthionine, poloxamer 188, acide phosphorique dilué, hydroxyde de sodium, eau pour préparations injectables.	Les autres ingrédients sont le mannitol, la méthionine, le poloxamère 188, l’acide phosphorique dilué, l’hydroxyde de sodium, l’eau pour préparations injectables.

LLM biases consist of LLM systems copying “statistical biases” from the data they work with. In translation, bias most often affects pronouns or job titles with two versions for female and male genders. For example, LLM systems may translate the job title “male nurse” as “female nurse” because there are statistically more female nurses.

Original (Turkish without gender indication)	LLM Translation (AI)
o bir hemşire	Il (masc.) est infirmière (fem.) (masc. = infirmier)

In the Turkish example, there is no indication of gender. In this situation, LLM translates the segment with the masculine pronoun (il), while the profession of nurse (hemşire) is rendered by the LLM system with the feminine gender.

Similarities and differences between NMT and LLM systems

Parameters	NMT	LLM
Architecture	Neural networks
	Transformer architecture
Translation method	Finds equivalents for the source text (text translation)	Rewrites text while preserving the meaning of the source (text generation)
Syntax	Sentence structure is maintained	Changes the sentence structure to make it more natural for the target language
Accuracy	Factual information is maintained	When translating, “hallucinations” and, rarely, bias may occur

Machine translation and AI translation are two powerful technologies, each with their own strengths and limitations. Neural Machine Translation (NMT) systems remain a reliable tool for tasks where terminology accuracy and preservation of the source text structure are important. At the same time, Large Language Models (LLMs) offer more natural and flexible translation, especially for creative tasks. However, choosing between them depends on specific needs. If strict adherence to the original is required, NMT is the better choice. If naturalness and adaptability are important, LLMs can provide better results. By combining the strengths of these systems and taking into account the features of each technology, you can achieve high-quality translation for virtually any task. The main thing is to understand which system to use and where to get the best results.