Private vs. Public Evaluations

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-private-vs-public-evals

1. What is the core idea behind "Private vs. Public Evaluations"?

   1. Public benchmarks get gamed. Private evaluations tell the truth but cannot be checked. Where is the balance? Third-party evaluators Organizations like METR (formerly ARC Evals) and the UK AI Safety Institute run closed evaluations on frontier models.
   2. Takeaway: the generalizable lesson
   3. Results: one figure or table plus the story it tells
   4. meta-learning

2. Which term best describes a foundational idea in "Private vs. Public Evaluations"?

   1. held-out set
   2. private eval
   3. third-party evaluation
   4. METR

3. A learner studying Private vs. Public Evaluations would need to understand which concept?

   1. private eval
   2. third-party evaluation
   3. held-out set
   4. METR

4. Which of these is directly relevant to Private vs. Public Evaluations?

   1. private eval
   2. held-out set
   3. METR
   4. third-party evaluation

5. Which of the following is a key point about Private vs. Public Evaluations?

   1. METR: evaluates dangerous capabilities and autonomy
   2. UK AISI: government body, access to pre-release models
   3. Apollo Research: focuses on deception and scheming
   4. NIST AISIC (US): emerging public-sector evaluator

6. Which of these does NOT belong in a discussion of Private vs. Public Evaluations?

   1. METR: evaluates dangerous capabilities and autonomy
   2. UK AISI: government body, access to pre-release models
   3. Apollo Research: focuses on deception and scheming
   4. Takeaway: the generalizable lesson

7. Which statement is accurate regarding Private vs. Public Evaluations?

   1. Grade each with explicit rubric (not gut feeling)
   2. Never post the prompts or the rubric online
   3. Collect 50-200 real tasks from your own workflow
   4. Re-run on every new model release

8. Which of these does NOT belong in a discussion of Private vs. Public Evaluations?

   1. Collect 50-200 real tasks from your own workflow
   2. Takeaway: the generalizable lesson
   3. Never post the prompts or the rubric online
   4. Grade each with explicit rubric (not gut feeling)

9. What is the key insight about "Your private eval" in the context of Private vs. Public Evaluations?

   1. Every serious AI team should keep a personal private eval — a small set of prompts that matter to them, which they never…
   2. Takeaway: the generalizable lesson
   3. Results: one figure or table plus the story it tells
   4. meta-learning

10. What is the key insight about "Small evals can still be useful" in the context of Private vs. Public Evaluations?

    1. Takeaway: the generalizable lesson
    2. Your 100-question personal eval will not have the statistical power of a 10,000-question benchmark — but it tests what y…
    3. Results: one figure or table plus the story it tells
    4. meta-learning

11. What is the recommended tip about "Ground your practice in fundamentals" in the context of Private vs. Public Evaluations?

    1. Takeaway: the generalizable lesson
    2. Results: one figure or table plus the story it tells
    3. Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more…
    4. meta-learning

12. Which statement accurately describes an aspect of Private vs. Public Evaluations?

    1. Takeaway: the generalizable lesson
    2. Results: one figure or table plus the story it tells
    3. meta-learning
    4. Public benchmarks are verifiable — anyone can reproduce the score. Private benchmarks are trustworthy — they cannot be gamed or leaked.

13. What does working with Private vs. Public Evaluations typically involve?

    1. Organizations like METR (formerly ARC Evals) and the UK AI Safety Institute run closed evaluations on frontier models.
    2. Takeaway: the generalizable lesson
    3. Results: one figure or table plus the story it tells
    4. meta-learning

14. Which of the following is true about Private vs. Public Evaluations?

    1. Takeaway: the generalizable lesson
    2. The big idea: the healthiest evaluation ecosystem mixes public (verifiable), private (trustworthy), and your own (relevant) measurements.
    3. Results: one figure or table plus the story it tells
    4. meta-learning

15. Which best describes the scope of "Private vs. Public Evaluations"?

    1. It is unrelated to foundations workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Public benchmarks get gamed. Private evaluations tell the truth but cannot be checked. Where is the
    4. It was deprecated in 2024 and no longer relevant