Summary
In this chapter, we explored the domain adaptation process for the BLOOM LLM, which is specifically tailored to enhance its proficiency in the financial sector, particularly in understanding and generating content related to Proxima’s product offerings. We began by introducing the concept of domain adaptation within the broader scope of transfer learning, emphasizing its significance in fine-tuning general-purpose models to grasp the intricacies of specialized fields.
The adaptation process involved integrating PEFT techniques into BLOOM and preprocessing a financial dataset for model training. This included standardizing text lengths through truncation and padding and tokenizing the texts for consistency in model input. The adapted model’s performance was then quantitatively assessed against a reference dataset using the ROUGE metric, providing insights into its ability to capture key financial terminologies and phrases. Qualitative evaluation by domain experts...
