Achieving Instruction Following with Minimal Fine-Tuning Data

A research lab has developed a very large language model that was pre-trained on a vast and diverse dataset from the internet. The lab now wants to adapt this model to be a helpful assistant that follows specific user commands, but they have a very limited budget for creating new training data. Based on the relationship between extensive pre-training and model adaptation, which of the following approaches is the most logical and resource-efficient for the lab to pursue?

Rationale for Efficient Instruction-Following Techniques

The extensive knowledge base acquired by a large language model during its pre-training on a massive dataset means that achieving reliable instruction-following behavior requires an equally massive and resource-intensive fine-tuning process.