Knowledge Cutoff Dates in LLMs Quiz

Answers and explanations appear as you take the quiz.

1. What is a model's knowledge cutoff?
   • The date the model stops working
   • The approximate date after which its training data contains little or no information
   • The date the model was released to the public
   • The date the vendor ends customer support
2. Why are models often unsure of, or underestimate, their own cutoff?
   • Vendors deliberately hide it from the model
   • Models delete their most recent data after training
   • Models count days incorrectly
   • The final months before the cutoff are thinly represented, because people keep writing about events long afterward
3. What is the most reliable way to find a model's knowledge cutoff?
   • Check the vendor's published model documentation
   • Ask the model directly
   • Count the words in its answers
   • Look at the model's logo
4. What simple fix corrects many cases of a model assuming the wrong current date?
   • Fine-tuning the model every week
   • Turning the model off and on
   • Putting the current date in the system prompt
   • Using shorter prompts
5. A chatbot with web browsing answers a question about last week's news. Which statement is accurate?
   • Its training cutoff has moved to last week
   • It can no longer make factual errors
   • It retrains itself on every query
   • Its training cutoff is unchanged; it is reading new sources and is only as accurate as the pages it retrieves
6. Why is retrieval usually preferred over fine-tuning for keeping facts current?
   • Fine-tuning is slower and more costly as a way to keep facts current
   • Fine-tuning is impossible for large models
   • Retrieval permanently changes the model's weights
   • Retrieval works without any documents
7. Which of these is a typical sign of stale knowledge?
   • The model refuses every question
   • The model switches to another language
   • The model names an outdated software version as the latest
   • The model's answers are unusually short
8. When building evals for cutoff-related problems, what should you include?
   • Only questions about events well before the cutoff
   • Questions whose correct answers changed after the cutoff
   • Questions with all dates removed
   • A question asking the model to state its cutoff