Question # 1
What is Transfer Learning in the context of Language Model (LLM) customization?
| A. It is where you can adjust prompts to shape the model's output without modifying its underlying weights.
| B. It is a process where the model is additionally trained on something like human feedback.
| C. It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
| D. It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.
|
C. It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
Explanation:
Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related task. Here’s a detailed explanation:
Transfer Learning: This involves taking a base model that has been pre-trained on a large dataset and fine-tuning it on a smaller, task-specific dataset.
Base Weights: The existing base weights from the pre-trained model are reused and adjusted slightly to fit the new task, which makes the process more efficient than training a model from scratch.
Benefits: This approach leverages the knowledge the model has already acquired, reducing the amount of data and computational resources needed for training on the new task.
References:
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A Survey on Deep Transfer Learning. In International Conference on Artificial Neural Networks.
Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Question # 2
Why should artificial intelligence developers always take inputs from diverse sources?
| A. To investigate the model requirements properly
| B. To perform exploratory data analysis
| C. To determine where and how the dataset is produced
| D. To cover all possible cases that the model should handle
|
D. To cover all possible cases that the model should handle
Explanation:
Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.
[: "Diverse data sources help AI models to generalize better and avoid biases." (MIT Technology Review, 2019), Comprehensive Coverage: By incorporating diverse inputs, developers ensure the model can handle various edge cases and unexpected inputs, making it robust and reliable in real-world applications., Reference: "Comprehensive data coverage is essential for creating robust AI models that perform well in diverse situations." (ACM Digital Library, 2021), Avoiding Bias: Diverse inputs reduce the risk of bias in AI systems by representing a broad spectrum of user experiences and perspectives, leading to fairer and more accurate predictions.,
Reference:
"Diverse datasets help mitigate bias and improve the fairness of AI systems." (AI Now Institute, 2018), , ]
Question # 3
What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?
| | A. LLMs receive input in human language and produce output in human language. | | B. LLMs are used to shrink the size of the neural network. | | C. LLMs are used to increase the size of the neural network. | | D. LLMs are used to parse image, audio, and video data. |
A. LLMs receive input in human language and produce output in human language.
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.
References:
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.
Question # 4
A team is working on improving an LLM and wants to adjust the prompts to shape the model's output. What is this process called?
| A. Adversarial Training
| B. Self-supervised Learning
| C. P-Tuning
| D. Transfer Learning
|
C. P-Tuning
Explanation:
The process of adjusting prompts to influence the output of a Large Language Model (LLM) is known as P-Tuning. This technique involves fine-tuning the model on a set of prompts that are designed to guide the model towards generating specific types of responses. P-Tuning stands for Prompt Tuning, where “P” represents the prompts that are used as a form of soft guidance to steer the model’s generation process.
In the context of LLMs, P-Tuning allows developers to customize the model’s behavior without extensive retraining on large datasets. It is a more efficient method compared to full model retraining, especially when the goal is to adapt the model to specific tasks or domains.
The Dell GenAI Foundations Achievement document would likely cover the concept of P-Tuning as it relates to the customization and improvement of AI models, particularly in the field of generative AI12. This document would emphasize the importance of such techniques in tailoring AI systems to meet specific user needs and improving interaction quality.
Adversarial Training (Option OA) is a method used to increase the robustness of AI models against adversarial attacks. Self-supervised Learning (Option OB) refers to a training methodology where the model learns from data that is not explicitly labeled. Transfer Learning (Option OD) is the process of applying knowledge from one domain to a different but related domain. While these are all valid techniques in the field of AI, they do not specifically describe the process of using prompts to shape an LLM’s output, making Option OC the correct answer.
Question # 5
What is Artificial Narrow Intelligence (ANI)?
| A. Al systems that can perform any task autonomously
| B. Al systems that can process beyond human capabilities
| C. Al systems that can think and make decisions like humans
| D. Al systems that can perform a specific task autonomously
|
D. Al systems that can perform a specific task autonomously
Explanation:
Artificial Narrow Intelligence (ANI) refers to AI systems that are designed to perform a specific task or a narrow set of tasks. The correct answer is option D. Here's a detailed explanation:
Definition of ANI: ANI, also known as weak AI, is specialized in one area. It can perform a particular function very well, such as facial recognition, language translation, or playing a game like chess.
Characteristics: Unlike general AI, ANI does not possess general cognitive abilities. It cannot perform tasks outside its specific domain without human intervention or retraining.
Examples: Siri, Alexa, and Google's search algorithms are examples of ANI. These systems excel in their designated tasks but cannot transfer their learning to unrelated areas.
References:
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
Question # 6
What is the significance of parameters in Large Language Models (LLMs)?
| A. Parameters are used to parse image, audio, and video data in LLMs.
| B. Parameters are used to decrease the size of the LLMs.
| C. Parameters are used to increase the size of the LLMs.
| | D. Parameters are statistical weights inside of the neural network of LLMs. |
D. Parameters are statistical weights inside of the neural network of LLMs.
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.
References:
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.
Question # 7
What is artificial intelligence?
| A. The study of computer science
| B. The study and design of intelligent agents
| C. The study of data analysis
| D. The study of human brain functions
|
B. The study and design of intelligent agents
Explanation:
Artificial intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that would normally require human intelligence. The correct answer is option B, which defines AI as "the study and design of intelligent agents." Here's a comprehensive breakdown:
Definition of AI: AI involves the creation of algorithms and systems that can perceive their environment, reason about it, and take actions to achieve specific goals.
Intelligent Agents: An intelligent agent is an entity that perceives its environment and takes actions to maximize its chances of success. This concept is central to AI and encompasses a wide range of systems, from simple rule-based programs to complex neural networks.
Applications: AI is applied in various domains, including natural language processing, computer vision, robotics, and more.
References:
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press.
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