Understanding Domain Adaptation for Large Language Models
In the previous chapter, we examined how Parameter-Efficient Fine-Tuning (PEFT) enhances large language models (LLMs) for specific tasks such as question-answering. In this chapter, we will be introduced to domain adaptation, a distinct fine-tuning approach. Unlike task-specific tuning, domain adaptation equips models to interpret language that’s unique to specific industries or domains, addressing the gap in LLMs’ understanding of specialized language.
To illustrate this, we’ll introduce Proxima Investment Group, a hypothetical digital-only investment firm aiming to adapt an LLM to its specific financial language using internal data. We’ll demonstrate how modifying the LLM to process the specific terminology and nuances typical in Proxima’s environment enhances the model’s relevance and effectiveness in the financial domain.
We’ll also explore the practical steps Proxima...
