Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.