Multi-Agent Simulations

It ought to be possible, in some scenarios (particularly resource constraints and model availability) to run a continuous multi-agent simulation to iteratively improve a draft translation.

One example I've implemented involves the following basic loop (each step is not necessarily blocked by the prior step):

• Main translation loop

  • Forward translation bot drafts a new translation instance using few-shot learning based on the available example translations drafted so far by humans

  • Back-translation bot provides a back-translation of the new instance

  • Evaluation bot provides feedback on how to improve, correct, or revise the instance

  • repeat process until the translation draft stabilizes (if it does!)

• Linguist bot builds and updates language resources, such as a multilingual project lexicon, a working grammar, machine grammar rules for output token biasing with the LLM, etc.

• Project manager bot brings stable drafts to human translation team for input, feedback, and correction. Corrected drafts are added to the pool of examples available to the main translation loop.

Early POC sample using GPTeam library