LLMs fixing mistakes evaluated in Keras chatbot arena on TPUs

AI Impact Summary

This article describes an experimental setup that tests how well LLMs can fix mistakes in real-time code-generation workflows by translating English requests into API calls. The study runs multiple sub-10B parameter instruction-tuned models on TPU-based hardware with model parallelism, showing that reliability varies by model size and family; smaller 1-2B models and older Vicuna variants struggle to produce correct API calls consistently. It highlights the importance of robust evaluation across models, leveraging TPU memory and sharding to enable parallel comparisons, and incorporating explicit feedback loops to steer outputs when mistakes are detected. The findings imply production teams should plan for guardrails and targeted model selection based on reliability for precise API actions in interactive assistants.