A developer is building a system to classify customer reviews as 'Positive', 'Negative', or 'Neutral'. Instead of using a traditional classification model, they are prompting a large, general-purpose text generation model to perform the task. The model is given the review: 'The battery life on this new phone is incredible!' Which of the following potential model outputs best illustrates why a separate 'label extraction' step is often required in this approach?

Example of an LLM Generating a Descriptive Negative Output for Polarity Classification

Debugging an LLM-based Classification Pipeline

Interpreting Text Generation Model Outputs for Classification