A startup is developing a new application that needs to perform three distinct functions: summarizing news articles, classifying customer feedback as 'Positive', 'Negative', or 'Neutral', and translating short phrases from English to Spanish. The development team is debating two approaches:

• Approach 1: Build and train three separate, specialized systems—one for summarization, one for classification, and one for machine translation. Each system would require its own architecture and task-specific training data.
• Approach 2: Use a single, large, pre-trained language model for all three functions, treating each one as a text generation task initiated by a specific instruction.

Based on the principle of reframing diverse problems into a unified format for a single model, which approach would be more efficient for the startup? Justify your choice by explaining the primary benefit of your selected approach compared to the alternative.