A financial tech company wants to deploy a chatbot on its mobile banking app to provide instant customer support. The primary requirements are that the chatbot must respond to user queries with minimal delay and consume as little battery and processing power as possible to ensure a good user experience across all devices. The company has a state-of-the-art, extremely accurate, but very large and computationally expensive language model. They decide to use this large model to train a much smaller, more compact model for the mobile app. Based on these priorities, which outcome represents the most successful application of this technique?

Analyzing LLM Performance Trade-offs

Evaluating Model Deployment Strategies