| [ | |
| { | |
| "question": "Which of the following best describes a Large Language Model (LLM)?", | |
| "answer_a": "A model specializing in language recognition", | |
| "answer_b": "A massive neural network that understands and generates human language", | |
| "answer_c": "A model exclusively used for language data tasks like summarization or classification", | |
| "answer_d": "A rule-based chatbot used for conversations", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "LLMs are typically:", | |
| "answer_a": "Pre-trained on small, curated datasets", | |
| "answer_b": "Trained on large text corpora to capture linguistic patterns", | |
| "answer_c": "Trained purely on translation tasks", | |
| "answer_d": "Designed to function solely with GPU resources", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "Which of the following is a common architecture for LLMs?", | |
| "answer_a": "Convolutional Neural Networks (CNNs)", | |
| "answer_b": "Transformer", | |
| "answer_c": "Recurrent Neural Networks (RNNs) with LSTM", | |
| "answer_d": "Support Vector Machines", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "What does it mean when we say LLMs are \"autoregressive\"?", | |
| "answer_a": "They regress to the mean to reduce variance", | |
| "answer_b": "They generate text by predicting the next token based on previous tokens", | |
| "answer_c": "They can only handle labeled data", | |
| "answer_d": "They can output text only after the entire input is known at once", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "Which of these is NOT a common use of LLMs?", | |
| "answer_a": "Summarizing content", | |
| "answer_b": "Generating code", | |
| "answer_c": "Playing strategy games like chess or Go", | |
| "answer_d": "Conversational AI", | |
| "correct_answer": "C" | |
| }, | |
| { | |
| "question": "Which of the following best describes a \"special token\"?", | |
| "answer_a": "A token that makes the model forget all context", | |
| "answer_b": "A model signature required for API calls", | |
| "answer_c": "A token that helps segment or structure the conversation in the model", | |
| "answer_d": "A token that always represents the end of text", | |
| "correct_answer": "C" | |
| }, | |
| { | |
| "question": "What is the primary goal of a \"chat template\"?", | |
| "answer_a": "To force the model into a single-turn conversation", | |
| "answer_b": "To structure interactions and define roles in a conversation", | |
| "answer_c": "To replace the need for system messages", | |
| "answer_d": "To store prompts into the model's weights permanently", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "How do tokenizers handle text for modern NLP models?", | |
| "answer_a": "By splitting text into individual words only", | |
| "answer_b": "By breaking words into subword units and assigning numerical IDs", | |
| "answer_c": "By storing text directly without transformation", | |
| "answer_d": "By removing all punctuation automatically", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "Which role in a conversation sets the overall behavior for a model?", | |
| "answer_a": "user", | |
| "answer_b": "system", | |
| "answer_c": "assistant", | |
| "answer_d": "developer", | |
| "correct_answer": "B" | |
| }, | |
| { | |
| "question": "Which statement is TRUE about tool usage in chat templates?", | |
| "answer_a": "Tools cannot be used within the conversation context.", | |
| "answer_b": "Tools are used only for logging messages.", | |
| "answer_c": "Tools allow the assistant to offload tasks like web search or calculations.", | |
| "answer_d": "Tools are unsupported in all modern LLMs.", | |
| "correct_answer": "C" | |
| } | |
| ] |