From Claude with some prompting
This diagram illustrates various fine-tuning techniques to improve the performance of large language models.
At the center, there is a Tuning Module connected to an Inference Module (for generating answers). The Tuning Module is linked to the Weight DataBase (Parameter), indicating that it fine-tunes the weights and parameters of the model.
On the left, there are Knowledge Base and Vector DataBase, which store the model’s knowledge and data.
In the top right, the RAG (Retrieval Augmented Generation) block retrieves relevant information from Domain Specific External Sources to augment the generation process.
The Prompt Engineering block involves Prompt Tuning to generate massive prompts with expert knowledge.
At the bottom, various parameter-efficient fine-tuning techniques are presented, such as PEFT, Fine Tuning, Bias Fine Tuning, Prefix Tuning, Adapter, and LoRA.
Regarding Prefix Tuning, the description “Attach a virtual prefix sequence” suggests that it involves adding virtual prompt tokens at the beginning of the input sequence.
Overall, this diagram comprehensively illustrates the integration of knowledge, prompt engineering, and diverse fine-tuning methods for enhancing large language models’ performance across various domains.
Lechuck Park 