Meta AI has released a comprehensive guide on methods for adapting large language models (LLMs), providing valuable insights for AI product teams looking to integrate these powerful tools into their projects.
In a blog post published on August 7, Meta AI's research team outlined various approaches to LLM adaptation, including pre-training, continued pre-training, fine-tuning, retrieval-augmented generation (RAG), and in-context learning (ICL).
The guide emphasises the importance of choosing the right adaptation method based on factors such as model capability requirements, training and inference costs, and available datasets. To assist developers, Meta AI provides a flowchart summarising their recommendations for selecting the most suitable LLM adaptation approach.
"Pre-training and continued pre-training, while vital parts of LLM development, are not recommended for teams with limited resources due to their computational intensity and susceptibility to catastrophic forgetting," the researchers noted.
Instead, the guide suggests fine-tuning, particularly parameter-efficient fine-tuning (PEFT), as a more viable approach for smaller teams. "Fine-tuning with smallish annotated datasets is a more cost-effective approach compared to pre-training(s) with unannotated datasets," the team explained.
For applications requiring extraction from dynamic knowledge bases, Meta AI recommends RAG as a potential solution. The guide also highlights ICL as the most cost-effective adaptation method, requiring no additional training data or computational resources.
Amir Maleki, an Applied Research Scientist at Meta AI, emphasised the iterative nature of creating LLM-based systems. "We advise starting with simple methods and gradually increasing complexity until your goals are achieved," Maleki stated.
As LLMs continue to evolve and find applications across diverse domains, Meta AI's guidelines provide a valuable resource for developers and researchers seeking to optimise their AI systems through thoughtful adaptation strategies.