2024 will be the year of MT Quality Estimation. Everyone who is using MT - and who isn’t these days? - will now want to use MTQE. Why? Because after all the excitement and hype around AI and LLMs in this past year, we want to put our feet on the ground and build trust in our MT setup. Besides, use cases tell us that one can easily save 30% or more on post-editing costs and efforts. TAUS is already processing around half-a-billion characters each month through our Estimate API, blocking the really bad machine translations from reaching our customers’ customers and flagging the good translations so that they don’t need to be sent to post-editors.

The question though is: what is the best way of implementing MTQE? There is a lot of talk about LLMs being capable now of also predicting the quality of MT. But how reliable, consistent and accurate are the LLMs really? And what about the cost and privacy issues around the set-up of QE with LLMs? The alternative is to implement your own controlled environment with pre-trained custom models, the approach offered by TAUS through the Estimate API.