What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?

What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?
A . Supervised learning feeds a large corpus of raw data into the Al system, while unsupervised learning uses labeled data to teach the Al system what output is expected.
B . Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.
C . Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.
D . Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.

View Answer

Answer: C

Explanation:

Supervised Learning: Involves using labeled datasets where the input-output pairs are provided. The AI system learns to map inputs to the correct outputs by minimizing the error between its predictions and the actual labels.

Reference: "Supervised learning algorithms learn from labeled data to predict outcomes." (Stanford University, 2019)

Unsupervised Learning: Involves using unlabeled data. The AI system tries to find patterns, structures, or relationships in the data without explicit instructions on what to predict. Common techniques include clustering and association.

Reference: "Unsupervised learning finds hidden patterns in data without predefined labels." (MIT Technology Review, 2020)

Application in LLMs: Supervised learning is typically used for fine-tuning models on specific tasks, while unsupervised learning is used during the initial phase to learn the broad features and representations from vast amounts of raw text.

Reference: "Large language models are often pretrained with unsupervised learning and fine-tuned with supervised learning." (OpenAI, 2021)