Artificial Intelligence

Choose the Correct Answer

Correct Answer : Option a

Explanation
Statement 1 is not correct: In terms of efficiency, small language models are generally faster to train and deploy than large language models. The smaller size and less complex architecture of small language models allow for quicker training times, enabling organizations to implement them more rapidly. Furthermore, small language models can be more easily integrated into existing systems and applications due to their lightweight nature, reducing the time and effort required for deployment.

Statement 2 is correct: Large Language Models (LLMs) typically require specialized hardware like Graphics Processing Units (GPUs) to handle their computationally intensive tasks efficiently due to the parallel processing capabilities of GPUs, which are crucial for managing the massive amounts of data involved in training and running large language models.

Statement 3 is not correct: Small language models are more cost-effective than large language models. The training and deployment of small language models requires fewer resources, making them more accessible to organizations with limited budgets or computational power. Small language models also tend to be less computationally expensive to run, requiring less powerful hardware and reducing infrastructure costs.