Mirela
Large language models (LLMs) are literally everywhere, including in the localization industry. They’re fast and surprisingly fluent, but sometimes they’re almost good enough. We understand the hype, because they can generate fluent text in seconds, can handle multiple languages at scale, and can reduce turnaround times in ways we wouldn’t have thought. However, it’s important we also understand what they struggle with, so stick around as we’re discussing the challenges for LLMs when used in localization.
Context is still hard to grasp
Localization usually happens with strings, fragments of text, marketing snippets, and content pulled from different systems. LLMs aren’t able to understand how one string connects to another, or how a phrase fits into a larger user journey. They process input in isolation unless you give them context. That’s why you might find subtle inconsistencies in the text.
In theory, you can feed an entire project’s style guide and glossary into the prompt. In reality, LLMs may ignore certain parts. Experts have named this “lost in the middle” syndrome. What they noticed was that the models tends to prioritize the beginning and the end of the instructions. For localizers, this means issues like inconsistent brand voice or, even “hallucinated” terms.
Can’t handle all languages the same
As some of you might already suspect, LLM performance isn’t consistent when it comes to language pairs. LLMs usually do great with high-resource languages like English, Spanish, French, because they have enough data to work with. On the other hand, they really struggle with low-resource language because there’s simply not enough training data.
If targeting both high-resource and low-resource languages, you will likely notice that the quality of your localization varies depending on the market. Some users will get a polished experience and others will receive something that clearly needs more human editing.
Cultural nuance remains a weak spot
As we know, language is tied by culture, but this is another challenge for LLMs. Many models are trained on data that leans heavily toward certain regions or languages, so their outputs can carry subtle cultural assumptions.. It’s usually not anything super obvious, more like tiny things such as the tone feeling a bit off or phrasing that doesn’t sound quite right to a local.
Local translators might read a translation and think “This is fine, but I wouldn’t say it like that.” The problem is that AI models lack this instinct. When we’re doing localization, we’re not simply translating. It’s important that the content feels native, personal.
Struggles with emotion
LLMs can be exceptionally good at giving you grammatically correct text, but they tend to produce safe, neutral texts. The issue here is that when you’re localizing marketing content, safe can sound boring, which is not what you want.
You might notice that when you’re translating humor or idiomatic expressions with a LLM, it’s hard for the system to keep the tone in the target language. It will preserve the structure and the meaning, but the result is kind of diluted. The result might not always resonate with your audience.
Domain-specific and technical content is tricky
LLMs are generalists at heart, unless we’re talking about domain-specific LLMs. And this is because their training data is heavy on casual chat but light on expert docs. Consequently, when you throw legal contracts, medical reports, engineering specs, and anything similar at them, they might mistranslate, use different variations of the same work (they won’t use the same terminology everywhere), or even hallucinate.
Where you fit in all this
LLMs are evolving, but they’re not quite there yet. They won’t be replacing localization professionals too soon, that’s for sure. The challenges for LLMs in localization stem from training data limitations and inherent model behaviors. That’s why they still require human oversight for reliable results.