The verified answer is A. Continuous pre-training. The company wants to expose an existing LLM to medical papers so the model becomes more familiar with medical topics, terminology, and writing patterns. AWS generative AI documentation defines continued pre-training as extending the pre-training phase of a foundation model by exposing it to additional unlabeled text from specific domains or corpora. It also explains that continued pre-training helps the model acquire deeper knowledge of new domains and adapt to domain-specific terminology and content types.
That exactly matches the scenario. Medical papers are typically domain text. The goal is not to train the model to follow a labeled input-output task, but to improve its domain familiarity and medical-language fluency. AWS security reference guidance also distinguishes continued pre-training from fine-tuning by stating that continued pre-training uses unlabeled data to enhance domain knowledge, while fine-tuning uses labeled data to optimize task-specific performance.
Supervised learning is incorrect because it requires labeled training examples, usually input-output pairs. The question only says the company has medical papers, not labeled prompts and responses.
Reinforcement learning is incorrect because it optimizes behavior based on rewards, feedback, or preference signals. The question does not describe reward modeling or human preference feedback.
Boosting is incorrect because boosting is an ensemble technique that combines weak learners to improve predictive performance. It is not the standard method for adapting an LLM to a specialized knowledge domain.
Because the company wants to familiarize an LLM with medical subject matter through medical papers, the correct technique is continuous pre-training.
