Part 6/13:
- Domain Specificity: General-purpose models often lack sufficient training on biomedical literature, leading to poor performance on tasks like Named Entity Recognition (NER) or paraphrasing.
Domain-Specific Fine-tuning and Evaluation
Their experiments reveal that models fine-tuned or trained on biomedical data outperform generic models. For example, GPT 3.5 substantially outperforms others in generating relevant summaries and precise responses, thanks to training on extensive web and scientific content.
However, models like Llama and Falcon, primarily trained for dialogue, sometimes produce amusing but unhelpful responses or fail to follow detailed instructions, especially on complex tasks requiring domain expertise.