Why We Need Human-in-the-Loop AI Translation

Generative AI translation services are powerful, but still yield the highest quality results with some strategic human intervention. All AI-powered language providers make room for human roles in AI, whether at the beginning, throughout the process, or at the end. Human-in-the-loop AI translation or content services are crucial for offering optimal AI-generated content and addressing generative AI limitations. Read on to learn about the three main types of human roles in AI translation, AI localization, and multilingual AI content services.

Human-in-the-Loop AI Translation and AI Training

The first key role linguists play is in AI training. Large Language Models (LLMs) must be trained to deliver optimal AI localization, translation, etc. This training occurs when generative AI language providers feed data to the LLM to help it execute AI solutions for every customer. Linguists often perform AI training services because they have the linguistic knowledge base (especially a deep understanding of natural language usage and pragmatic language competence) to help with the data collection and annotation required for comprehensive LLM training. Knowing what material to feed to LLMs demands expertise in linguistic nuances, grammar and syntax, industry-specific terminology, cultural norms, etc.

Linguists also assist with another part of AI training: error analysis. Creating a strong corpus of training material is vital, but it’s also often not enough to fully train an LLM. Linguists can review an LLM’s output samples and identify inaccuracies and hallucinations. This step requires extensive linguistic, cultural, and/or industry experience. The linguists will then identify new materials or prompts to help train the LLM, so it doesn’t make the identified errors (or similar errors) anymore.

Human-in-the-Loop AI Translation and Post-Editing

Complicated or high-stakes translations may require a human reviewer for post-editing to build AI trust. These cases include material that will be:

• Submitted for regulatory purposes
• Hyper-visible for customers
• Used for annual reports
• Done in rare language pairs
• Full of terminology saturation or technical terms

Post-editing can actually occur at any point in the human-in-the-loop AI translation process. Linguists might perform post-editing after reviewing a small LLM translation sample. Then, the linguist gives the LLM feedback before it performs the rest of the translation or localization. The reviewer may also execute post-editing repeatedly throughout the process. Or, they may only perform it after the LLM has translated or localized everything. The frequency and timing of a linguist’s post-editing can be customized to the specific project. These are some of the items linguists look for in post-editing: 

• Cultural sensitivity
• Bias
• Hateful content
• Inaccurate translations
• Incorrect terminology
• Syntactical errors
• Run-on sentences
• Readability
• Poorly used or translated idioms
• Places where idioms should be used