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.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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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