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Question 17 - D-GAI-F-01 discussion

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What is the significance of parameters in Large Language Models (LLMs)?

A.
Parameters are used to parse image, audio, and video data in LLMs.
Answers
A.
Parameters are used to parse image, audio, and video data in LLMs.
B.
Parameters are used to decrease the size of the LLMs.
Answers
B.
Parameters are used to decrease the size of the LLMs.
C.
Parameters are used to increase the size of the LLMs.
Answers
C.
Parameters are used to increase the size of the LLMs.
D.
Parameters are statistical weights inside of the neural network of LLMs.
Answers
D.
Parameters are statistical weights inside of the neural network of LLMs.
Suggested answer: D

Explanation:

Parameters in Large Language Models (LLMs) are statistical weights that are adjusted during the training process. Here's a comprehensive explanation:

Parameters: Parameters are the coefficients in the neural network that are learned from the training data. They determine how input data is transformed into output.

Significance: The number of parameters in an LLM is a key factor in its capacity to model complex patterns in data. More parameters generally mean a more powerful model, but also require more computational resources.

Role in LLMs: In LLMs, parameters are used to capture linguistic patterns and relationships, enabling the model to generate coherent and contextually appropriate language.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog.

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asked 16/09/2024
Sujit Singh
34 questions
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