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Free D-GAI-F-01 Exam Braindumps (page: 6)

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PDF with 58 Questions

What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?

  1. LLMs receive input in human language and produce output in human language.
  2. LLMs are used to shrink the size of the neural network.
  3. LLMs are used to increase the size of the neural network.
  4. LLMs are used to parse image, audio, and video data.

Answer(s): A

Explanation:

The primary function of Large Language Models (LLMs) in Natural Language Processing (NLP) is to process and generate human language. Here's a detailed explanation:
Function of LLMs: LLMs are designed to understand, interpret, and generate human language text. They can perform tasks such as translation, summarization, and conversation. Input and Output: LLMs take input in the form of text and produce output in text, making them versatile tools for a wide range of language-based applications. Applications: These models are used in chatbots, virtual assistants, translation services, and more, demonstrating their ability to handle natural language efficiently.


Reference:

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems.



What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

  1. To customize the model for a specific task by feeding it task-specific content
  2. To feed the model a large volume of data from a wide variety of subjects
  3. To use the model in a production, research, or test environment
  4. To randomize all the statistical weights of the neural networks

Answer(s): C

Explanation:

Inferencing in the lifecycle of a Large Language Model (LLM) refers to using the model in practical applications. Here's an in-depth explanation:
Inferencing: This is the phase where the trained model is deployed to make predictions or generate outputs based on new input data. It is essentially the model's application stage. Production Use: In production, inferencing involves using the model in live applications, such as chatbots or recommendation systems, where it interacts with real users. Research and Testing: During research and testing, inferencing is used to evaluate the model's performance, validate its accuracy, and identify areas for improvement.


Reference:

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. Chollet, F. (2017). Deep Learning with Python. Manning Publications.



What strategy can an organization implement to mitigate bias and address a lack of diversity in technology?

  1. Limit partnerships with nonprofits and nongovernmental organizations.
  2. Partner with nonprofit organizations, customers, and peer companies on coalitions, advocacy groups, and public policy initiatives.
  3. Reduce diversity across technology teams and roles.
  4. Ignore the issue and hope it resolves itself over time.

Answer(s): B

Explanation:

Partnerships with Nonprofits: Collaborating with nonprofit organizations can provide valuable insights and resources to address diversity and bias in technology. Nonprofits often have expertise in advocacy and community engagement, which can help drive meaningful change.


Reference:

"Nonprofits bring expertise in social issues and can aid companies in addressing diversity and bias." (Harvard Business Review, 2019)
Engagement with Customers: Involving customers in diversity initiatives ensures that the solutions developed are user-centric and address real-world concerns. This engagement can also build trust and improve brand reputation.

"Customer engagement in diversity initiatives helps align solutions with user needs." (McKinsey & Company, 2020)
Collaboration with Peer Companies: Forming coalitions with other companies helps in sharing best practices, resources, and strategies to combat bias and promote diversity. This collective effort can lead to industry-wide improvements.

"Collaboration with peer companies amplifies efforts to address industry-wide issues of bias and diversity." (Forbes, 2021)
Public Policy Initiatives: Working on public policy can drive systemic changes that promote diversity and reduce bias in technology. Influencing policy can lead to the establishment of standards and regulations that ensure fair practices.

"Engaging in public policy initiatives helps shape regulations that promote diversity and mitigate bias." (Brookings Institution, 2020)



What is P-Tuning in LLM?

  1. Adjusting prompts to shape the model's output without altering its core structure
  2. Preventing a model from generating malicious content
  3. Personalizing the training of a model to produce biased outputs
  4. Punishing the model for generating incorrect answers

Answer(s): A

Explanation:

Definition of P-Tuning: P-Tuning is a method where specific prompts are adjusted to influence the model's output. It involves optimizing prompt parameters to guide the model's responses effectively.


Reference:

"P-Tuning adjusts prompts to guide a model's output, enhancing its relevance and accuracy." (Nature Communications, 2021)
Functionality: Unlike traditional fine-tuning, which modifies the model's weights, P-Tuning keeps the core structure intact. This approach allows for flexible and efficient adaptation of the model to various tasks without extensive retraining.

"P-Tuning maintains the model's core structure, making it a lightweight and efficient adaptation method." (IEEE Transactions on Neural Networks and Learning Systems, 2022) Applications: P-Tuning is particularly useful for quickly adapting large language models to new tasks, improving performance without the computational overhead of full model retraining.
"P-Tuning is used for quick adaptation of language models to new tasks, enhancing performance efficiently." (Proceedings of the AAAI Conference on Artificial Intelligence, 2021)






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