This questionnaire contains 50 multiple-choice questions designed to assess your knowledge of Large Language Models (LLMs) and Generative AI. Select the best answer for each question.
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1. What does LLM stand for in the context of AI?
A) Limited Learning Machine
B) Large Language Model
C) Linear Logic Method
D) Language Learning Mechanism
2. Which of the following is NOT a foundational architecture for modern LLMs?
A) Transformer
B) LSTM
C) GPT
D) Convolutional Neural Network
3. What is "prompt engineering"?
A) A technique for optimizing hardware to process LLM queries faster
B) The process of designing and refining inputs to get desired outputs from LLMs
C) A hardware engineering approach for AI accelerators
D) The process of creating new LLM architectures
4. What technique is primarily used to align language model outputs with human values and preferences?
A) Supervised fine-tuning
B) Reinforcement Learning from Human Feedback (RLHF)
C) Generative Adversarial Training
D) Self-supervised learning
5. What is "hallucination" in the context of LLMs?
A) When an LLM experiences system failure due to memory overflow
B) When an LLM generates content that appears plausible but is factually incorrect or made up
C) A technique to visualize the inner workings of neural networks
D) When an LLM accidentally reveals its training data
6. What is "few-shot learning" in the context of LLMs?
A) Training a model with a very small dataset
B) Providing a few examples in the prompt to guide the model's response
C) Using only a few parameters in the model architecture
D) Running the model on low-powered devices
7. Which paper introduced the Transformer architecture that revolutionized NLP?
A) "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"
B) "Attention Is All You Need"
C) "GPT-3: Language Models are Few-Shot Learners"
D) "Deep Residual Learning for Image Recognition"
8. What is the primary mechanism that allows Transformers to process sequences effectively?
A) Convolutional layers
B) Recurrent connections
C) Self-attention mechanism
D) Reinforcement learning
9. What is "tokenization" in the context of LLMs?
A) Converting digital assets into blockchain tokens
B) Breaking text into smaller units (tokens) for processing
C) A security measure to prevent prompt injection
D) Assigning computational resources to different model components
10. Which of the following is NOT typically a component of the Transformer architecture?
A) Multi-head attention
B) Feed-forward neural networks
C) Convolutional layers
D) Layer normalization
11. What does "fine-tuning" refer to in the context of LLMs?
A) Optimizing a model's hyperparameters
B) Taking a pre-trained model and further training it on a specific dataset for a particular task
C) Manually adjusting the weights of a neural network
D) Cleaning and preprocessing the training data
12. What is "perplexity" in language modeling?
A) A measure of how confused the model is about its predictions
B) A metric that quantifies how well a model predicts a sample, with lower values indicating better performance
C) The ability of a model to handle complex grammatical structures
D) The ratio of correct to incorrect predictions
13. Which technique helps mitigate catastrophic forgetting during fine-tuning?
A) Dropout
B) Parameter-Efficient Fine-Tuning (PEFT)
C) Batch normalization
D) Gradient clipping
14. What is "LoRA" in the context of LLMs?
A) A benchmark dataset for evaluating language models
B) A low-rank adaptation technique for efficient fine-tuning
C) A large-scale repository of AI models
D) A language representation assessment framework
15. Which problem is NOT typically addressed by instruction fine-tuning?
A) Making models better at following specific directions
B) Improving hardware efficiency
C) Reducing hallucinations
D) Making responses more aligned with human expectations
16. What is "retrieval-augmented generation" (RAG)?
A) A technique that combines generative models with an information retrieval system
B) A method for retrieving training examples that were most influential for a given prediction
C) A system that generates retrieval queries automatically
D) An augmented reality application powered by generative AI
17. What is the primary advantage of quantization for LLMs?
A) It improves model accuracy
B) It reduces the computational and memory requirements
C) It increases the model's knowledge
D) It enables training on larger datasets
18. What is a "context window" in LLMs?
A) The graphical user interface where users interact with the model
B) The maximum length of text the model can process in a single forward pass
C) A technique for focusing attention on specific parts of the input
D) The time period during which a model was trained
19. What are "embeddings" in the context of LLMs?
A) Mathematical representations of text in a high-dimensional vector space
B) Techniques for embedding LLMs into hardware devices
C) Special tokens inserted into the input sequence
D) Visualizations of neural network activations
20. Which metric is specifically designed to evaluate text summarization quality?
A) BLEU
B) ROUGE
C) F1 Score
D) Area Under Curve (AUC)
21. What is "greedy decoding" in text generation?
A) A technique that always selects the token with the highest probability at each step
B) A method that maximizes the length of generated text
C) An approach that prioritizes rare tokens in the vocabulary
D) A strategy that optimizes for computational efficiency
22. What is "temperature" in the context of LLM text generation?
A) A measure of how hot the GPU gets during inference
B) A parameter that controls the randomness of token selection
C) The emotional tone of the generated text
D) A metric for evaluating how "heated" or controversial the content is
23. Which technique helps prevent LLMs from generating harmful content?
A) Encryption
B) Content filtering
C) Reinforcement Learning from Human Feedback (RLHF)
D) Both B and C
24. What is "chain-of-thought" prompting?
A) A technique where multiple LLMs are chained together
B) Encouraging the model to show its reasoning process step by step
C) A method for tracking the provenance of information in generated text
D) A way to link multiple queries into a single conversation
25. What is "knowledge distillation" in the context of LLMs?
A) Extracting factual knowledge from LLM outputs
B) A process where a smaller model is trained to mimic a larger model
C) Concentrating the model's knowledge on specific domains
D) Removing incorrect information from training data
26. Which of the following is a common issue with using LLMs for code generation?
A) They can only generate code in Python
B) They tend to generate code that looks plausible but may contain subtle bugs
C) They cannot understand code comments
D) They always produce code with optimal performance
27. What is "multimodal AI"?
A) AI systems that can work with multiple programming languages
B) AI systems that can process and generate different types of data (e.g., text, images, audio)
C) AI models that use multiple different architectures combined
D) AI trained on multiple datasets
28. Which approach does NOT typically help in reducing LLM hallucinations?
A) Retrieval-augmented generation
B) Using higher temperature settings
C) Asking the model to reason step by step
D) Fine-tuning on high-quality data
29. What is "prompt injection" in the context of LLM security?
A) Adding examples to a prompt to improve model performance
B) An attack where adversarial inputs are designed to manipulate the model into producing unintended outputs
C) A technique for optimizing prompt design
D) Adding system instructions to guide model behavior
30. What is "in-context learning"?
A) Learning new facts from the internet during inference
B) The ability of LLMs to adapt to new tasks based on examples provided in the prompt
C) Continual training of models during deployment
D) A technique for adapting to user preferences over time
31. What is "model distillation" used for in LLMs?
A) Removing offensive content from model outputs
B) Creating smaller, more efficient models that approximate the capabilities of larger models
C) Filtering out low-quality data from training datasets
D) Extracting factual knowledge from pre-trained models
32. What is a key difference between decoder-only and encoder-decoder architectures?
A) Decoder-only models can only generate text, while encoder-decoder models can do both understanding and generation
B) Encoder-decoder models are always smaller than decoder-only models
C) Decoder-only models are used primarily for image generation
D) Encoder-decoder models cannot be fine-tuned
33. What is "prompt tuning" in the context of LLMs?
A) Manually refining prompts to get better results
B) A parameter-efficient fine-tuning method that only updates a small set of task-specific vectors
C) Automatically generating prompts for specific tasks
D) Adjusting the model's temperature and other generation parameters
34. Which of these is NOT a common technique for controlling LLM outputs?
A) System prompts
B) Temperature adjustment
C) Top-k sampling
D) Graph traversal
35. What is "constitutional AI"?
A) AI systems programmed to follow a country's constitution
B) An approach that uses an initial set of rules or principles to guide model behavior
C) AI systems specifically designed for legal applications
D) Models that are deployed in government institutions
36. What does the "alignment problem" refer to in AI?
A) The challenge of making sure neural networks are properly initialized
B) The challenge of ensuring AI systems act in accordance with human values and intentions
C) The difficulty in aligning different layers in deep neural networks
D) The problem of ensuring text is properly aligned in generated documents
37. What is "compute-optimal scaling" in the context of LLMs?
A) Using the minimum amount of computation required to train a model
B) A principle that suggests how to optimally allocate compute between model size, dataset size, and training time
C) A technique for optimizing GPU utilization during inference
D) A method for determining the optimal hardware configuration for LLM deployment
38. What is a "foundation model"?
A) The first model in a series of increasingly advanced AI systems
B) A large model trained on a broad dataset that can be adapted to a wide range of downstream tasks
C) A model that serves as the mathematical foundation for other models
D) The architectural blueprint for building neural networks
39. What is "KL divergence" commonly used for in training LLMs?
A) Measuring the distance between probability distributions in distillation or alignment
B) Evaluating the knowledge level of the model
C) Optimizing kernel loading in distributed training
D) Key-Level encryption of model weights
40. Which technique is NOT typically used to extend an LLM's context window?
A) Position interpolation
B) Rotary position embeddings
C) Sparse attention mechanisms
D) Gradient boosting
41. What is "multi-query attention"?
A) The ability to handle multiple user queries simultaneously
B) An efficient attention variant where all attention heads share the same key and value projections
C) A technique for searching multiple databases with a single query
D) An attention mechanism that utilizes multiple GPUs
42. What is "mixture of experts" (MoE) in the context of LLMs?
A) Combining predictions from multiple separate models
B) A model architecture where different specialized sub-networks (experts) handle different input tokens
C) Ensembling advice from different human experts in the training data
D) Using crowd-sourced human feedback for evaluation
43. What is a primary challenge in evaluating LLM outputs?
A) The high computational cost of running evaluations
B) The subjective nature of judging the quality of text for many tasks
C) The lack of standardized metrics
D) The inability to compare outputs across different models
44. What is "mechanistic interpretability" in the context of LLMs?
A) Understanding how users interpret the outputs of language models
B) Building mechanical interfaces for AI systems
C) Reverse-engineering the internal mechanisms and computational patterns of neural networks
D) Interpreting AI behavior in mechanical engineering applications
45. What does "PEFT" stand for in the context of LLMs?
A) Performance Evaluation for Fine-Tuning
B) Parameter-Efficient Fine-Tuning
C) Preprocessing Engine for Framework Training
D) Parallelized Execution of Fast Transformers
46. Which of the following is NOT a common application of LLMs?
A) Text summarization
B) Code generation
C) Semiconductor manufacturing
D) Customer support automation
47. What is "data contamination" in the context of LLM evaluation?
A) When toxic content enters the training data
B) When evaluation data has been included in the training data, leading to artificially high performance
C) When models leak private information from training data
D) When different datasets are mixed incorrectly during preprocessing
48. What is "zero-shot learning" in the context of LLMs?
A) Training a model without any data
B) The ability to perform a task without any specific examples of that task in the prompt
C) A technique where models are trained with zero hyperparameters
D) The capacity to perform tasks with zero mistakes
49. What is "emergent ability" in large language models?
A) The capability of models to perform tasks they weren't explicitly trained for
B) Abilities that only appear when multiple models are combined
C) Features that emerge spontaneously during inference
D) Capabilities that only appear at a certain scale of model size or data
50. Which technique is most directly associated with making LLMs more factual and reducing hallucinations?
A) Increasing model size
B) Retrieval-Augmented Generation (RAG)
C) Higher temperature sampling
D) Mixture of Experts
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ANSWER KEY
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1. B) Large Language Model
2. D) Convolutional Neural Network
3. B) The process of designing and refining inputs to get desired outputs from LLMs
4. B) Reinforcement Learning from Human Feedback (RLHF)
5. B) When an LLM generates content that appears plausible but is factually incorrect or made up
6. B) Providing a few examples in the prompt to guide the model's response
7. B) "Attention Is All You Need"
8. C) Self-attention mechanism
9. B) Breaking text into smaller units (tokens) for processing
10. C) Convolutional layers
11. B) Taking a pre-trained model and further training it on a specific dataset for a particular task
12. B) A metric that quantifies how well a model predicts a sample, with lower values indicating better performance
13. B) Parameter-Efficient Fine-Tuning (PEFT)
14. B) A low-rank adaptation technique for efficient fine-tuning
15. B) Improving hardware efficiency
16. A) A technique that combines generative models with an information retrieval system
17. B) It reduces the computational and memory requirements
18. B) The maximum length of text the model can process in a single forward pass
19. A) Mathematical representations of text in a high-dimensional vector space
20. B) ROUGE
21. A) A technique that always selects the token with the highest probability at each step
22. B) A parameter that controls the randomness of token selection
23. D) Both B and C
24. B) Encouraging the model to show its reasoning process step by step
25. B) A process where a smaller model is trained to mimic a larger model
26. B) They tend to generate code that looks plausible but may contain subtle bugs
27. B) AI systems that can process and generate different types of data (e.g., text, images, audio)
28. B) Using higher temperature settings
29. B) An attack where adversarial inputs are designed to manipulate the model into producing unintended outputs
30. B) The ability of LLMs to adapt to new tasks based on examples provided in the prompt
31. B) Creating smaller, more efficient models that approximate the capabilities of larger models
32. A) Decoder-only models can only generate text, while encoder-decoder models can do both understanding and generation
33. B) A parameter-efficient fine-tuning method that only updates a small set of task-specific vectors
34. D) Graph traversal
35. B) An approach that uses an initial set of rules or principles to guide model behavior
36. B) The challenge of ensuring AI systems act in accordance with human values and intentions
37. B) A principle that suggests how to optimally allocate compute between model size, dataset size, and training time
38. B) A large model trained on a broad dataset that can be adapted to a wide range of downstream tasks
39. A) Measuring the distance between probability distributions in distillation or alignment
40. D) Gradient boosting
41. B) An efficient attention variant where all attention heads share the same key and value projections
42. B) A model architecture where different specialized sub-networks (experts) handle different input tokens
43. B) The subjective nature of judging the quality of text for many tasks
44. C) Reverse-engineering the internal mechanisms and computational patterns of neural networks
45. B) Parameter-Efficient Fine-Tuning
46. C) Semiconductor manufacturing
47. B) When evaluation data has been included in the training data, leading to artificially high performance
48. B) The ability to perform a task without any specific examples of that task in the prompt
49. D) Capabilities that only appear at a certain scale of model size or data
50. B) Retrieval-Augmented Generation (RAG)
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SCORING
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Count the number of correct answers:
0-15 correct: Small knowledge in LLMs/Gen AI
- You're at the beginning of your journey in understanding large language models and generative AI.
- Consider exploring foundational concepts like transformers, tokenization, and basic prompt engineering.
16-30 correct: Intermediate knowledge in LLMs/Gen AI
- You have a solid understanding of core concepts in LLMs and generative AI.
- To advance further, focus on more technical aspects like fine-tuning, evaluation methods, and architectural details.
31-45 correct: Expert knowledge in LLMs/Gen AI
- You demonstrate advanced understanding of LLMs and generative AI, including technical implementation details and optimization techniques.
- Continue exploring cutting-edge research and specialized applications.
46-50 correct: Master/Yoda of LLMs/Gen AI
- You possess comprehensive knowledge of the field, including advanced concepts, architectural details, and latest developments.
- You likely have hands-on experience building or fine-tuning models and can contribute to advancing the state of the art.
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