Spaces:
Running
Running
File size: 3,834 Bytes
31157e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
[
{
"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"
}
] |