Simulated GAN Evaluation: A Method for Assessing AI-Generated Translations

#translation #ai

Here’s an idea I had for translation quality checking.

The process begins by prompting for a translation using n examples and a directive to complete the translation pairs. The entire prompt, along with the AI’s completion, is then passed to the evaluator. The evaluator’s task is to determine which translation was generated by the AI. This may require fine-tuning a discriminator model on the gold-standard pairs.

The concept, inspired by Generative Adversarial Networks (GANs), involves an evaluator attempting to distinguish AI-drafted translations from a list of real translations. While it doesn’t function exactly like a GAN, the idea is to use a similar principle for evaluation.

The method could potentially be used to refine the prompts or the models themselves, by replacing the evaluator with a separate AI. Initial implementation will use Language Models (LLMs) to gauge the viability of the concept. If successful, this approach could be extended to train GANs for specific languages.