5 Critical AI Language Translation Gaps to Watch in 2025
Since ChatGPT dropped in 2022, large language models (LLMs) have been all the rage. ChatGPT and similar tools like Claude and Gemini have been heralded as high-powered productivity tools that can streamline your workflow and make many of our day-to-day work tasks significantly easier.
And while it may be true that they can make our jobs a little bit easier, that doesn’t mean they can make our jobs easy. In the field of translation, LLMs have been widely applied alongside machine translation (MT) tools to produce fluent and accurate translations from one language into the next. But organizations looking to translate their content should be careful when employing tools like ChatGPT to translate texts — though they may yield accurate literal translations, these translations may not be fully adequate for your needs as a business.
In this blog post, we’ll take a look at five key areas in which AI tools like ChatGPT are still lacking when it comes to translations. From their struggle to understand context to their tendency to editorialize, these are some of the most common issues you’ll want to look out for if you plan on using any of these LLMs to translate your content.
1. Lacking cultural or historical context
Although LLMs can produce pretty accurate literal translations, they’re still not great for situations where you need to translate culturally nuanced language like idioms and other common expressions.
According to a 2024 study, AI still struggles to make sense of (and thus, translate accurately) culturally nuanced phrases and ambiguities that human linguists are better prepared to parse out. So while these tools may excel with simple texts like routine forms and paperwork, you’ll still need a human in the loop when translating more complex texts like marketing brochures and web pages.
2. No self-correction or monitoring for errors
If you’ve ever tried to correct ChatGPT after it produces inaccurate output, you know just how hard it is to get LLMs to correct themselves. But making corrections and editing our work is a key part of a translator’s work — language service professionals catch errors in the moment and adjust accordingly all the time.
In a 2023 study, researchers found that LLMs have trouble “self-correcting” — that is, refining future output based, either on their “inherent capabilities” or on feedback to previous responses.
According to the study, LLMs struggle to “self-correct their responses without external feedback, and at times, their performance even degrades after self-correction.” That means that these tools may produce errors and then repeat those errors throughout a given text. If you’re using these tools for translation, you’ll need to make sure somebody knowledgeable about both the target and source language is carefully reviewing the output and editing it to minimize these errors.
3. Concerns about data privacy
LLMs have raised a wide range of data privacy concerns, and organizations working in highly regulated industries like healthcare and law should be leery of using them for translation tasks. Different industries and locations have different standards of data privacy, so it’s important to be aware of what is and isn’t acceptable for your circumstances.
For example, LLMs are generally not HIPAA-compliant — healthcare organizations using this tool to translate texts into another language must make sure to mask any patient health information (PHI) such as name, date of birth, social security number, etc. before putting it into the tool. That means healthcare organizations must be careful to either manually or through automations (AvantShield) scan the original text for any such information and remove it entirely from the text, to avoid issues with HIPAA compliance.
4. Implicit biases of LLMs
In addition to data privacy, another common concern that experts have raised about AI is its implicit biases. All sorts of AI models have drawn criticism for their biases — take, for example, Amazon’s recruiting tool that showed a bias against women applicants in the search and hire process.
Such biases may also come up in LLMs performing translation tasks. LLMs are particularly notorious for tone-shifting and editorializing in their translations. They may shift the overall tone of a text to more closely align with its own standards of tone — for example, rephrasing a common yet pejorative buzzword like “woke” as something more neutral, like “aware of social inequality.” The translation of this phrase would in turn lose the connotation understood by the original terminology, harming the overall quality of the translation.
5. Difference in quality across languages
Although LLMs can produce highly accurate and fluent text in languages like English, Spanish and French, the same isn’t true for all languages.
These tools are trained on massive amounts of text in various languages, primarily taken from the internet — there’s more available input for languages like English and Spanish than there is for Pashto, for example. Languages with more training data will be easier to translate between; on the other hand, LLMs will struggle to produce accurate translations in languages that don’t have a large digital presence — like indigenous languages of the Americas, for example.
Summary
LLMs may be useful tools, but they’re still far from adequate replacements for human linguists. Instead, human linguists should take a smart, balanced approach to incorporating AI tools into their workflow. By correctly identifying the proper use scenarios of AI in the translation process, we can leverage AI tools in an effective way that speeds things up without sacrificing the overall quality. Here’s why:
LLMs struggle to understand cultural or historical context that may be relevant to certain texts, making transcreation and localization tasks nearly impossible with AI alone.
These tools aren’t great at correcting themselves, even when they receive external feedback.
Organizations working in highly regulated industries like healthcare need to be careful not to violate data privacy laws.
AI is known for holding implicit biases which may affect the quality and content of the final translation.
The overall quality of translation will vary by language — languages that do not have a significant digital presence have less training data to draw from and produce an accurate translation.
At Avantpage, we’re well aware of the limits of AI tools in the translation process. We have a technologically driven translation workflow, with human linguists involved for quality control. If you need translation or localization services, contact us today at [email protected] or (530) 750-2040.