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question: "What is the primary goal of Intern-S1 as described in the abstract?",
options: [
"To create a general-purpose language model for everyday tasks",
"To develop a specialized multimodal model for scientific research with expertise in analyzing multiple science modalities",
"To compete with closed-source models only in mathematical problem-solving",
"To build a vision-language model focused on natural images"
],
correct: 1
},
{
question: "How many activated parameters and total parameters does Intern-S1 have?",
options: [
"14 billion activated, 120 billion total",
"28 billion activated, 241 billion total",
"56 billion activated, 482 billion total",
"10 billion activated, 80 billion total"
],
correct: 1
},
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question: "What percentage of the 5T training tokens came from scientific domains?",
options: [
"25%",
"50%",
"75%",
"Over 50% (specifically >2.5T tokens)"
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correct: 3
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question: "What innovative technique did Intern-S1 use during its post-training reinforcement learning phase?",
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"Mixture-of-Rewards (MoR)",
"Dynamic Reward Shaping (DRS)",
"Multi-task Gradient Aggregation (MGA)"
],
correct: 1
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question: "Which of the following scientific tasks does the paper claim Intern-S1 surpasses closed-source state-of-the-art models in?",
options: [
"Image captioning and object detection",
"Molecular synthesis planning, reaction condition prediction, predicting thermodynamic stabilities for crystals",
"Real-time video translation and voice cloning",
"Web navigation and e-commerce recommendation"
],
correct: 1
},
{
question: "According to the introduction, why is scientific research considered important for AGI development?",
options: [
"Because it requires large amounts of labeled data",
"Because it drives fundamental breakthroughs in human society and demands rigorous reasoning across diverse scientific modalities",
"Because it relies heavily on social media data",
"Because it can be solved with simple rule-based systems"
],
correct: 1
},
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question: "What type of model is Intern-S1 described as?",
options: [
"A unimodal transformer model",
"A multimodal Mixture-of-Experts (MoE) model",
"A convolutional neural network for image classification",
"A recurrent neural network for time-series analysis"
],
correct: 1
},
{
question: "What is the key limitation of current open-source models in scientific domains, according to the paper?",
options: [
"They are too slow to train",
"They lack sufficient computational resources",
"Their progress lags significantly behind popular domains like math and code, and there's a substantial gap compared to closed-source models",
"They don't support English"
],
correct: 2
},
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question: "Which of these benchmarks is mentioned as part of the scientific evaluation suite for Intern-S1?",
options: [
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"SmolInstruct, ChemBench, MatBench, SFE, Physics",
"GLUE and SuperGLUE",
"MNIST and CIFAR-10"
],
correct: 1
},
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question: "Where can the Intern-S1 models be accessed?",
options: [
"GitHub.com/intern-s1",
"Hugging Face at https://huggingface.co/internlm/Intern-S1",
"OpenAI's model zoo",
"Google AI Hub"
],
correct: 1
}
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