What is "in-context learning" in the realm of large Language Models (LLMs)?
Options:
A.
Teaching a mode! through zero-shot learning
B.
Training a model on a diverse range of tasks
C.
Modifying the behavior of a pretrained LLM permanently
D.
Providing a few examples of a target task via the input prompt
Answer:
D
Explanation:
Explanation:
In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. References: [2307.12375] In-Context Learning in Large Language Models Learns Label …](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models] (https://arxiv.org/abs/2307.07164)
Question 9
What is the primary goal of machine learning?
Options:
A.
Enabling computers to learn and improve from experience
B.
Explicitly programming computers
C.
Creating algorithms to solve complex problems
D.
Improving computer hardware
Answer:
A
Explanation:
Explanation:
Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. Machine learning algorithms can adapt to new data and situations and improve their performance over time2. References: Artificial Intelligence (AI) | Oracle
