Instruction Tuning Quiz

Answers and explanations appear as you take the quiz.

1. Without instruction tuning, how might a raw base model respond to the prompt 'What is the capital of France?'
   • It always refuses to answer
   • It returns an error
   • It might continue with more questions or related text instead of answering
   • It answers perfectly every time
2. Instruction tuning is best described as:
   • Reinforcement learning from human feedback
   • Pretraining on raw web text
   • Adjusting the temperature parameter
   • Supervised fine-tuning on instruction-and-response pairs
3. How does supervised instruction tuning differ from reward-model-based RLHF in the published InstructGPT recipe?
   • It is exactly the same optimization stage
   • It trains against reference responses, while the later preference stages use comparisons and a reward model
   • It requires no training examples
   • It only changes a sampling temperature
4. Why do instruction-tuning researchers evaluate models on held-out tasks and instructions?
   • To assess generalization beyond the examples used for training
   • To prove that every unseen task will succeed
   • To avoid checking any generated responses
   • To guarantee the same accuracy for every task mixture
5. Which is the typical order of training stages for a modern chat assistant?
   • RLHF, then pretraining, then instruction tuning
   • Instruction tuning, then pretraining
   • Only pretraining, nothing else
   • Pretraining, then instruction tuning, then optional RLHF
6. What does LIMA's curated-data experiment support as a research conclusion?
   • Exactly 1,000 examples are enough for every model and task
   • More data can never help any instruction-tuned model
   • Carefully selected small datasets can be effective in an evaluated configuration
   • Only synthetic examples can train an assistant
7. Which training stage did the original LIMA experiment omit while using its 1,000 curated prompt-response pairs?
   • Reinforcement learning and human preference modeling
   • Supervised response loss
   • A pretrained base model
   • Prompt-response examples
8. A tuned model follows the requested answer format. What does that behavior alone fail to establish?
   • That an output format was requested
   • That every factual statement in the answer is correct
   • That the model produced a response
   • That the response can be inspected