How TrainAI validated LLM translations across 14 rare language pairs with human expertise

When a multinational technology organization needed to evaluate how well its LLMs translated content across rare and underrepresented language pairs, they turned to TrainAI by RWS. 

The organization creates open-source AI and language technologies designed to support as many languages as possible. But for many rare language pairs, they faced a critical gap: they couldn't confidently assess whether their large language model (LLM) translations were conveying meaning accurately.

Having worked with the RWS localization team for years, the client turned to RWS's TrainAI team to apply specialist linguistic expertise across multiple regions and scripts. This would ensure as much translation quality, consistency and cultural accuracy as possible. 

The stakes were high. This project pushed into genuinely difficult territory.

The core need was straightforward but technically complex. The organization needed to evaluate whether LLM translations conveyed the same knowledge as the original sentences. 

They didn’t need to be word-for-word accurate, but they did need to preserve meaning. Someone reading only the translated sentence should gain the same understanding as someone reading the original. 

What's more, the 14 language pairs selected for the project weren't simply translations between English and a rare language. Often, evaluators had to compare writing in one rare language to writing in another rare language, making validation exponentially more difficult. 

Validation was especially challenging because many pairs involved two low-resource languages rather than a major “pivot” language. 

The TrainAI team identified 14 rare language pairs to focus on based on the resources available. These included pairs such as Zulu to Venda, Sorani Kurdish to Western Persian, and Spanish to Basque, among others. 

Unfortunately, large datasets for rare languages don’t exist, at least not yet. Regional variations can also be significant enough to change meaning, as a single language might have three distinct regional variants. This limits both LLM training data quality and the availability of reliable benchmarks for evaluation. 

In addition, some of these languages rely heavily on metaphor, while their translation counterparts are literal. When translating between two rare languages with no major language on either end, there is often no stable reference point, making automated validation less reliable.

TrainAI used a rigorous but straightforward approach. Evaluators received sentences one at a time through TrainAI's platform and scored them on a 1 to 5 scale. A score of 1 meant the translation was complete nonsense – entirely different meaning or the LLM endlessly repeating words. Scores of 2-5 represented increasing levels of semantic equivalence. 

The client then calculated an overall paragraph score based on these individual sentence scores. The key methodological insight: one person's judgment isn't enough. TrainAI required three evaluators to reach a minimum rate of agreement on each piece of content. This approach helped filter out individual bias and ensured that regional linguistic variants were properly understood rather than incorrectly flagged as errors. 

TrainAI received thousands of submissions and maintained obsessive quality oversight. The team used statistical analysis tools to study patterns in the data. When an evaluator applied the methodology incorrectly, the pattern was analyzed, and the evaluator's approach was corrected going forward.

Subjectivity inevitably crept in, especially with regional linguistic variants that legitimately differed from one another. Some of these differences weren't errors; they were simply the nature of language diversity. 

The project also faced operational friction that required careful coordination to protect quality and timelines. In some regions, evaluators were reluctant to adopt new tools or invest time in learning unfamiliar systems. In others, power outages and local infrastructure limitations caused intermittent disruptions, making data collection and submission inconsistent. 

TrainAI even had to get creative about how to move data back and forth, thinking outside the box to manage dozens of variables and logistics across geographically dispersed teams.

TrainAI reviewed 10% of submissions upfront as a quality gate, stopping if more than 75% scored at level 1 (complete failure). This sometimes triggered a pivot to human translation for language pairs where LLMs consistently underperformed. 

Agreement scores landed between 70–85%, an ideal range that reflects independent critical judgment. Near-perfect agreement can signal groupthink, where evaluators align too closely instead of assessing meaning individually. In this case, the scores showed that evaluators were actively engaging with the content rather than rubber-stamping results. 

The client received debugged, validated translation data backed by multi-reviewer consensus. This methodology for maintaining quality helped catch problems early when they were easier to fix. 

Most importantly, human expertise created a bridge between LLMs’ limits and the type of scrutiny that rare language pairs require to maintain accuracy. 

For organizations building global AI systems, the takeaway is straightforward: rare and underrepresented languages demand human judgment to maintain meaning, cultural nuance and context, as well as automated solutions to operate projects at scale. 

TrainAI’s linguistic expertise and disciplined methodology transformed uncertainty into measurable insight. The client now has validated data to confidently improve its open-source language technologies worldwide.