Important Questions on Generative Artificial Intelligence
In our past discussions on Generative AI, we have tackled various questions and provided detailed explanations. Now, we further look into the crucial questions shaping this innovative technology's future. This blog post will explore the fundamentals of prompt engineering, Architectures of various Gen AI models and the various principle of Gen AI. Regardless if you're an AI enthusiast, a student, or simply curious about technological advancements, join us as we uncover answers that will equip you to navigate the dynamic world of Generative AI.
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1. In prompt engineering, why is it important to specify the desired format or structure of the response?
b) It helps guide the LLM to produce responses that match the desired criteria.
c) Specifying the format is not necessary and may be limit the LLM's creativity.
d) LLMs are automatically trained to follow specific response formats without user input.
Answer: b) It helps guide the LLM to produce responses that match the desired criteria.
Explanation:
If we specify the desired format in a prompt the response given by LLM's will be presented in structured way and as per our expectations. For example, we can specifically ask for a paragraph of 300 words or a detailed paragraph, list of bullet points or a table. By defining these output expectations, we can get output in desired format.
2. Which of the following is a closed source large language model?
b) T5
Answer: a) GPT-3
Explanation:
GPT-3 expanded as Generative Pre-trained Transformer version 3 is developed by AI firm OpenAI. Usually source code of closed source model is not made available publicly as it is a closed-source LLM. T5 is a Text-to-Text Transfer model based on Transformer architecture, it is developed by Google. OpenLM is a project for open-source language modeling.
3. What is one of the key challenges faced by Gen Al models in terms of consistency?
b) Producing unrealistic outputs
Answer: c) Achieving consistent results with the same input
Explanation:
Gen AI models are having a probabilistic nature, it means they generate their outputs based on probabilities they have learned from the data during their training. This can lead to production of inconsistencies results when we use same prompt multiple times. These inconsistencies include change of writing tone or style between responses. These models may go off-topic or lose focus when producing response for the same prompt.
4. What is a common metric for evaluating the quality of generated images?
b) BLEU Score
Answer: (a) Inception Score
Explanation:
Inception Score (IS) is designed to assess the quality and diversity of images generated by Gen AI models. It has a pre-trained image classifier to determine how realistic is the generated image. The BLEU Score is used to determine quality of machine-translated text. F1 Score is primarily used to gauge performance of classification AI models.
5. Which principle emphasizes the need to collect data from a variety of sources and demographics?
b) Transparency
Answer: d) Data Diversity
Explanation:
Data Diversity is the principle that ensures AI models are trained on all-inclusive ethical data, free from any biases. It emphasizes on the importance of collecting data from sources of diverse background and demographics. The main goal is to mitigate biases and ensure that AI models will perform and behave ethically with users from diverse backgrounds and in all situations.
6. Which of the following is a disadvantage of open source large language models?
b) Potential for less controlled use
Answer: b) Potential for less controlled use
Explanation:
The code as well as generated output data is freely available for the open source LLM's. This helps to keep transparency of the process and individuals can customize it as per their needs. However, there is a disadvantage too, being open source anyone can use and modify it, sometimes people's with malicious intent modify it for their own benefits.
7. What is the difference between Generative Al and predictive Al?
b) Generative Al is much more efficient than discriminative Al comparatively.
Answer: a) Generative AI creates new content, while predictive Al classifies existing content.
Explanation:
Generative AI is known to create new data (text stories, poems, images, music, other visual content such as art etc.) Example GPT-3, Google Bard (Gemini) for text generation, Dall-E for image generation. Predictive AI which is also known as Discriminative AI, It analyzes existing data to classify new data.
8. What is chain-of-thought prompting?
b) Asking a series of related questions to guide the model.
Answer: b) Asking a series of related questions to guide the model
Explanation:
The technique of breaking down a complex prompt into step by step questions is called a Chain-of-thought prompting. A more accurate and comprehensive output can be obtained from LLM model by asking a step by step questions. It is like guiding a model through reasoning process, to obtain more accurate and relevant detailed response.
9. What challenge does generative Al face with respect to data?
b) Overfitting on low-quality data
Answer: c) Both A and B
Explanation:
AI models requires training on good quality data in large quantities to gain ability to generate expected outputs. However, access to high quality data is always not possible due to economical, political, social and some times due to ethical reasons. On the other hand if the training data contains noisy, biased and poor quality information the model will gain inefficient abilities which may create and amplify biased, poor quality outputs.
10. Choose the Generative Al models for language from the following.
b) Generative Pre-trained Transformer
Answer: b) Generative Pre-trained Transformer
Explanation:
While as per recent advancements Generative Adversarial Networks (GAN's) and Diffusion models (DALL E, Midjourney) have more or less ability to generate natural language responses. However Generative Pre-trained Transformer (GPT) based models are specifically designed for language related tasks, for example dialogue generation, copywriting, translation, summarization etc.
11. Which is NOT a limitation of using closed source LLMS?
b) Consistently using closed source LLMs for various use cases can incur high costs
Answer: c) Closed source LLMs are less accurate and perform poorly compared to open-source LLMs
Explanation:
The mentioned statement is not a limitation of the closed source LLM's as accuracy and performance of a LLM model is dependent on it's architecture, data in which it is trained on and overall development process. Based on Open Source and closed source aspect we cannot determine accuracy and performance. Remaining options are the limitations of closed source LLM models as they are proprietary so use involve costs, users cannot customize it and processing is done on the server so data is being sent to owner company for the processing.
12. Which of the following is a characteristic of a foundation model?
b) It is specialized for a specific task.
Answer: c) It is trained on a large and diverse dataset.
Explanation:
Foundation models are mostly trained on massive amounts of datasets, which are from diverse sources. The aim is to impart an ability to capture a thorough understanding of topic it being trained on. They are trained to be a all in one general-purpose model used for various tasks rather than specialized for a single task.
13. In the context of image generation, what is the purpose of the discriminator in a GAN?
b) To evaluate and determine the quality of generated images
Answer: (b) To evaluate and determine the quality of generated images
Explanation:
The discriminator has a job validation and checking. It is imparted with ability to distinguish between real and fake images by specific training. It basically compares images generated by the generator and provides feedback to the Generator. The provided feedback is used by generator to improve its ability to produce images.
14. Generative Al models are statistical models that learn to generate new data by analyzing existing data. Say True or False
b)TRUE
Answer: b)TRUE
Explanation:
Yes the statement is true, Gen AI models are specialized statistical models which are trained on data to help it gain ability to analyze patterns, structures, internal relationships and intricate details in existing data. They use this gained ability to produce new fresh data, similar to the training data.
15. Which of the following is NOT a strategy used in prompt engineering?
a) Using precise language in prompts
b) Providing clear context and output expectations in the input prompts c) Adding irrelevant information to the prompt d) Experimenting with different prompt lengths
b) Providing clear context and output expectations in the input prompts
Answer: c) Adding irrelevant information to the prompt
Explanation:
Adding irrelevant information in the prompt could mislead the AI model and cause it to generate the poor quality output. However it is recommended to add exact information, context of the query being asked, output format expectations etc. This helps AI model to generate relevant and good quality output.
16. What is one challenge related to the interpretability of generative Al models?
a) Lack of research interest
b) Inability to train models c) Models often function as "black boxes" d) Overly complex mathematical operations
b) Inability to train models
Answer: c) Models often function as "black boxes"
Explanation:
The Black Box Nature is termed as system in which only input and output is visible, we are not able to understand how system arrived at a particular output. Generative AI models are considered as Black Boxes as it contains lot of parameters and after permutations and combinations of it an output is generated, so it difficult to understand how it arrives at the output as conclusion.
17. What does the principle of fairness in Gen Al entail?
a) Optimizing the model architecture to reduce bias
b) Ensuring equitable treatment and addressing biases in outputs c) Promoting diversity within development teams d) None of the above
b) Ensuring equitable treatment and addressing biases in outputs
Answer: b) Ensuring equitable treatment and addressing biases in outputs
Explanation:
As per the Fairness principle in Gen AI, AI-generated content should not discriminate against individuals or group of individuals based on aspects like gender, age, race, social or economic status. It tells to actively keep removing biases in the training data and fine tuning algorithms to ensure unbiased content as a output.
18. What purpose do Fairness Measures serve in Al product development?
a) To ensure discriminatory practices
b) To increase complexity without benefits c) To enforce biases in decision-making d) To ensure equal treatment across diverse user groups
b) To increase complexity without benefits
Answer: d) To ensure equal treatment across diverse user groups
Explanation:
The main goal of Fairness principles in AI systems is to recognize and eradicate biases in it. The principle ensures models will not discriminate against people or passes any unfair statements about certain groups of individuals. The AI system will become trustworthy if it treats all people equal and benefit everyone.
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