How accurate are LLMs when translating text? It’s a fair question, one that’s becoming increasingly important as AI translation tools keep getting better and companies look for new ways to optimize costs.
But you’re probably here because you’re wondering if LLMs are accurate enough to use without a human in the loop.In this article, you’ll learn how LLMs translate text, how their approach differs from traditional machine translation, and how accurate they really are across different languages and types of content.
🤖 Discover valuable content on LLM and AI translation
At Lokalise, we keep a close eye on how translation is evolving, especially with AI and LLMs changing the game. The truth is, AI translation tools keep getting better. But better doesn’t always mean good enough. If you’re curious about where it’s all headed, make sure to explore more of our insights on the blog.
What are LLMs and how do they translate text
LLMs, or large language models, are a type of AI trained on huge amounts of text data. They learn patterns in language, grammar, meaning, and structure.
Unlike older machine translation tools that rely on phrase-based matching or statistical rules, LLMs generate translations by predicting what words should come next based on context. That means they don’t just look at individual words, but also consider tone, sentence structure, and the overall flow.
🧠Good to know
Instead of swapping words from one language to another, LLMs try to recreate meaning in the target language. This allows them to handle more natural-sounding translations. However, it also makes them prone to guesswork when context is unclear. This is why feeding your AI translation tool with context matters a lot.
How LLM translation differs from traditional machine translation
Traditional machine translation tools have come a long way. They moved from rule-based systems to neural machine translation (NMT), which powers tools like DeepL and Google Translate today. These systems are trained specifically to map meaning between language pairs, using bilingual data.
LLMs, on the other hand, are general-purpose models trained on a mix of monolingual and multilingual text. They weren’t built specifically for translation, but they’ve learned to translate by noticing patterns in massive amounts of text.
You can see the pros and cons of LLMs and machine translation in a glance in the table below.
| LLMs (large language models) | NMT (neural machine translation) | |
| Pros | – More fluent, natural-sounding translations – Better at handling longer context and full paragraphs – Can adapt tone and rephrase content | – Great for word-for-word translations – Trained on large bilingual datasets – Can help with translation consistency |
| Cons | – Prone to hallucinations or made-up content – May change meaning if context isn’t clear enough – Quality depends on the language pair | – Can sound robotic or too literal – Struggles with context when facing a longer text – Less flexible and accurate with tone or nuance |
| Best for | – Creative and long-form content – Content where tone of voice matters | – Technical or repetitive content – Formal documentation where there’s literal meaning |
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How accurate are LLMs at translating different types of content?
The short answer? It depends.
Some top-performing LLMs can be surprisingly accurate. When using an LLM for translation, it can deliver human-level output for certain types of content, but then be inconsistent or unreliable for others. Their accuracy varies based on the domain, length, tone, and language pair involved. Check out the breakdown below.
Conversational or informal content
LLMs tend to do well with conversational or informal. Take GPT-4 for example, which has shown strong performance when translating casual or everyday language.
In a recent study comparing GPT-4 translations with those of professional translators, researchers found that GPT-4 performed on par with junior human translators in terms of total error count, particularly in general-domain text.
This means that for blog posts, social content, or internal docs, LLMs may already be good enough.
Technical or domain-specific content
Translation accuracy drops when LLMs are tasked with medical, legal, or other domain-specific texts. The same study found that GPT-4’s error rate was significantly higher in professional and technical domains compared to expert human translators.
If the input required subject-matter expertise or terminology precision, it’s best to leave it with humans, or to provide enough relevant context to the LLM before post-editing.
Creative or literary content
LLMs struggle most with translating creative or stylistic text. A 2024 paper analyzing literary translations found that LLMs produced more literal and less diverse outputs compared to human translators.
While AI captures surface meaning, it often misses tone, symbolism, or cultural nuance. This is why the hybrid approach still beats opting for exclusively AI or human translations.
Low-resource languages
For languages with less available training data (e.g., such as Amharic, Lao, or Māori), performance of LLMs drops. A 2025 study found that LLMs showed frequent semantic errors in low-resource language translation tasks.
Again, human oversight is needed. Despite improvements, the study acknowledges that LLMs still struggle with capturing cultural nuances in translation.
🗒️ Key takeaway
LLMs are already competitive for general translation use cases. But for specialized, sensitive, or low-resource content, they still need a safety net. It’s best to have a human expert involved, and think about building terminology bases, glossaries, or adding another translation QA layer.
What affects LLM translation accuracy
Three main factors affect LLM translation accuracy: context, language pair, and the complexity of the source text. Each of these affects how well the model can make sense of the content and deliver a translation that sounds right. The same principles apply to LLM code translation, where understanding structure and context is just as critical.
Context
LLMs are designed to be context-aware, and that’s part of their strength. The best AI I translation tools don’t just translate sentence by sentence. Instead, they look at the bigger picture.
However, when context is limited or vague (e.g., isolated strings, headlines without explanations), translation accuracy drops. Models might guess the wrong meaning or choose the wrong tone for translated content.
📚 Further reading
Ever wondered how AI translation works? Read our easy-to-understand guide to learn what happens behind the curtain when you click “Translate”.
Language pair
LLMs perform better on some languages than others. They’re most accurate when translating between high-resource languages (like English, Spanish, or French). This makes sense because they’ve seen more examples during training.
Low-resource or morphologically rich languages (e.g., Yoruba, Uzbek, or Inuktitut) are more error-prone. And imagine what happens when translating between two low-resource languages? That’s where LLMs struggle because there’s often not enough data to translate confidently.
💡 The complexity of categorizing languages as “low-resource”
According to a 2024 paper, “The Zeno’s Paradox of ‘Low-Resource’ Languages”, labeling a language as ‘low-resource’ is not always accurate because it overlooks deeper issues. We need to think about community involvement, digital presence, and moving benchmarks, and all of these factors are hard to measure. The paper invites us to rethink how we define and support these languages in AI.
Content complexity
Simple, declarative sentences are easier for LLMs to translate. But complex or technical content are more challenging.
- Long, nested sentences can confuse the LLM
- Industry jargon or abbreviations can also be confusing
- Creative or idiomatic phrases often get flattened or over-literal when translated
In fact, the more specialized or creative the content is, the more likely you’ll need a human translator (or at least a second pair of eyes for post-editing and quality assurance).
Real-world examples: where LLMs get it right (and wrong)
LLMs can produce fast, fluent translations, but they don’t always get the right meaning across.
Here are some real-world examples that show both the strengths and blind spots of LLMs when translating into or from German.
Examples where LLM gets translations right
Straightforward content
- Original (English): You can update your password anytime in your account settings.
- LLM output (German): Sie können Ihr Passwort jederzeit in den Kontoeinstellungen aktualisieren.
Translation is clear, correct, and sounds natural to a native speaker. There’s no need for post-editing at all.
Short UI strings
- Original (English): Cancel / Next steps / Save changes
- LLM output (German): Abbrechen / Nächster Schritt / Änderungen speichern
Standard translation, clear and accurate, and consistent with the UX.
Product explanation
- Original (English): Our tool helps teams collaborate faster and manage content at scale.
- LLM output (German): Unser Tool hilft Teams, schneller zusammenzuarbeiten und Inhalte in großem Umfang zu verwalten.
Matches tone and intent, accurate, nothing gets lost in translation.
Examples where LLM gets translations wrong
Literal translation of idioms
- Original (English): We’ve got your back.
- LLM output (German): Wir haben deinen Rücken.
Here, the LLM translation doesn’t make any sense. A human would say something like Du kannst dich auf uns verlassen which translates to You can count on us; even though in this case, the translation is not literal, it still conveys the meaning.
Misinterpretation of source text
- Original (English): The actual numbers were lower than expected.
- LLM output (German): Die aktuellen Zahlen waren niedriger als erwartet.
If there’s ambiguity involved, LLMs might struggle. In this example, aktuell means current, not actual. The correct word in German would be tatsächlichen.
Complex sentence structure
- Original (English): I know that the update will be ready tomorrow.
- LLM output (German): Ich weiß, dass das Update wird morgen bereit sein.
Here, the word order is incorrect in the subordinate clause. In German, the verb goes at the end in sentences like this. So, if we want to follow proper German sentence structure and make sure the translation sounds natural, it would go like this: Ich weiß, dass das Update morgen bereit sein wird.
As you can see, human oversight is still very much needed.
Common mistakes LLMs make in translation
Even when the output sounds fluent, LLMs can make subtle translation mistakes that can sometimes be very serious. The most common ones include over-literal translations, wrong word choice because of ambiguity in meaning, tone mismatch, and hallucinations.
- Over-literal translations: LLMs sometimes stick too closely to the source text, especially when dealing with idioms or expressions
- Wrong word choice: When a word has multiple meanings, LLMs can pick the wrong one if context is missing or unclear
- Tone mismatch: LLMs may translate the content correctly but miss the intended tone
- Hallucinations: LLMs can “hallucinate” and generate text that sounds plausible but wasn’t in the source (this is rare with direct sentence-level translation, but it still happens)
These errors don’t always stand out at first glance, so it’s useful to be aware of them in advance.
🧠 Good to know
LLMs don’t usually make spelling or grammar mistakes. Their output looks polished. But their errors are often deeper and include picking the wrong meaning, misjudging tone, or failing to adapt to context. That’s why human review still matters, especially for anything that goes out in public or is high-stakes (e.g., legal documents).
How LLMs are improving through feedback and training
LLMs today are far from static. Their translation abilities are getting better. Models are getting larger, they’re learning from feedback, and they’re being refined more intentionally.
But how does this actually happen?
Fine-tuning on real data
Many LLMs are improved after their initial training through a process called fine-tuning. This involves training the model further on specific types of content. Think customer support chats, legal contracts, or multilingual documentation. This helps improve accuracy in those specific domains.
Targeted training helps LLMs handle specialized vocabulary, understand tone better, and reduce errors that general-purpose models often make.
Reinforcement learning from human feedback (RLHF)
Another major driver of LLM improvement is reinforcement learning from human feedback (RLHF). Humans review model outputs and rank them based on quality. The model then learns which translations work the best.
This is how models like GPT-4 became significantly better than their predecessors. They learn what good looks like, according to real people.
🧠 Did you know?
LLMs are continuously gathering feedback “in the wild”. Every time users flag errors, correct outputs, or choose alternative suggestions (especially in tools with interactive UIs), that feedback is used to improve the next version of the model.
Training LLMs on diverse data
The more diverse and representative the training data, the better the model gets at understanding different languages, writing styles, and cultural context. Training on content from different domains, dialects, and regions also helps reduce bias (very important from an ethical standpoint).
When to trust LLM translation vs. when to use a human
It’s important to know when you can trust the LLM translation with confidence, and when it’s smarter to bring in a human. Check the table below for a quick overview.
| Type of content | LLM translation | Human translation | Explanation |
| General content with clear structure | ✔️ | ❌ | LLM is great for translating FAQs, product descriptions, internal docs, or blog posts |
| Low-stake content | ✔️ | ❌ | If you need to understand the gist of an article or translate a quick message, LLMs are perfect for this |
| First draft for translations | ✔️ | ❌ | LLMs can be used to generate fast first drafts that can be reviewed by humans (common in AI localization workflows) |
| High-stakes and/or public content | ❌ | ✔️ | Legal contracts, medical information, press releases, all need human review |
| Highly nuanced content | ❌ | ✔️ | When tone, nuance, or humor matters, you need a native speaker |
| Low-resource and/or complex language pairs | ❌ | ✔️ | LLMs struggle with less-represented languages |
🗒️ Key takeaway
You can trust LLMs for speed, scale, and everyday content, but be aware of their limits. When accuracy, context, or impact matter, a human translator still makes the difference between good enough, and truly right and impactful translations.
Final verdict: are LLMs ready for reliable translation?
LLMs have come a long way and they’re constantly changing how we think about translation. For general content, familiar language pairs, and everyday use cases, they’re more than capable. They’re fast, fluent, and constantly improving.
But are they reliable? Depends on what’s at stake.
If you need precision, cultural nuance, or domain expertise, LLMs still need a safety net. They can support professional translators, speed up workflows, and handle bulk tasks, but they’re not ready to fully replace human judgment in high-risk or high-touch scenarios.
In short, LLMs and AI translation tools are ready to help, but they’re not yet ready to do it all alone.
Lokalise AI combines the speed of LLM-powered translation with the control and context your team needs. You can translate up to 10x faster for 80% less costs and with no loss in translation quality.
Make sure to sign up for a free trial and see it for yourself.
