Demystifying domain adaptation – understanding its history and importance
In the context of generative LLMs, domain adaptation specifically tailors models such as BLOOM, which have been pre-trained on extensive, generalized datasets (such as news articles and Wikipedia entries) for enhanced understanding of texts from targeted sectors, including biomedical, legal, and financial fields. This type of refinement can be pivotal as LLMs, despite their vast pre-training, may not inherently capture the intricate details and specialized terminology inherent to these domains. This adaptation involves a deliberate process of realigning the model’s learned patterns to the linguistic characteristics, terminologies, and contextual nuances prevalent in the target domain.
Domain adaptation operates within the ambit of transfer learning. In this broader paradigm, a model’s learnings from one task are repurposed to improve its efficacy on a related yet distinct task. This approach...
