Debunking the Myth: Fine-Tuning Large Language Models (LLMs) for New Tasks and Domains
The notion that fine-tuning a large language model (LLM) necessitates retraining the entire model for every new task or domain is a common misconception. In reality, this is far from the truth. The reality is that only the task-specific layers of the LLM need to be fine-tuned for new tasks or domains, using techniques such as few-shot learning and parameter-efficient transfer learning.
When it comes to LLMs, the model architecture is typically composed of multiple layers, with different layers responsible for distinct functions. For example, the lower layers are often responsible for capturing syntactic and semantic features, while the higher layers focus on more abstract tasks such as reasoning and inference.
To fine-tune an LLM for a new task or domain, we can focus on updating only the higher layers, which are responsible for the specific task or domain. This approach, known as ...
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