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Rohit Ghumare
dda194f840
Every "Test Your Understanding" quiz placed the correct answer in option B.
Across the 2026 questions in 338 quiz files the correct answer sat at index 1
in 61.5% of cases (uniform would be ~25%), and 107 files had every answer at B,
making the quizzes guessable without reading them.
scripts/debias_quizzes.py rewrites each question's option order with a
deterministic, content-seeded permutation and updates the correct index to
follow the moved answer. It is idempotent: options are canonicalised to a sorted
base before permuting, so re-running produces byte-identical output. Questions
whose options reference each other by position ("all of the above", "both A and
B") are left untouched. The correct-answer value, the option set, and every
explanation are preserved exactly; only order and the index change.
Result: A 23.8% / B 26.3% / C 23.5% / D 26.4%.
The script doubles as a CI guard: `--check` exits non-zero if any quiz is not
de-biased, wired into the curriculum workflow so new lessons cannot regress.
Fixes #368
79 lines
2.6 KiB
JSON
79 lines
2.6 KiB
JSON
{
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"lesson": "84-refusal-evaluation",
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"title": "Capstone 84 — Refusal Evaluation",
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"questions": [
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{
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"stage": "pre",
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"question": "What does under-refusal measure?",
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"options": [
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"How often the model refuses prompts labeled safe",
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"The cost of the API call per prompt",
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"How often the model answers prompts labeled unsafe",
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"How many tokens the model emits per response"
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],
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"correct": 2,
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"explanation": ""
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},
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{
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"stage": "pre",
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"question": "Why use a mock LLM rather than a real model for this lesson?",
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"options": [
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"Because real models are not allowed in capstones",
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"Because mock LLMs are more accurate than real ones",
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"So the same input yields the same output across runs, making code changes attributable to known causes",
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"Because the lesson requires GPU hardware"
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],
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"correct": 2,
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"explanation": ""
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},
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{
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"stage": "check",
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"question": "Given safe prompt count = 30, refused safe count = 5, what is over-refusal rate?",
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"options": [
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"0.30",
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"0.17",
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"0.50",
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"0.05"
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],
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"correct": 1,
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"explanation": ""
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},
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{
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"stage": "check",
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"question": "What does Expected Calibration Error (ECE) measure here?",
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"options": [
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"The fraction of refusals on unsafe prompts",
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"The gap between the model's stated confidence and its observed accuracy, binned across confidence ranges",
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"The total token count across all responses",
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"The number of regex rules that fired"
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],
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"correct": 1,
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"explanation": ""
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},
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{
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"stage": "check",
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"question": "Why does the framework join unsafe prompts against the lesson 82 taxonomy?",
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"options": [
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"To re-run the lesson 82 corpus loader",
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"To raise the severity of every prompt by one",
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"To report under-refusal per attack category so the team sees which boundary the model leaks on",
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"To remove duplicates"
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],
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"correct": 2,
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"explanation": ""
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},
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{
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"stage": "post",
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"question": "Why does the framework return both under-refusal and over-refusal rather than a single safety score?",
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"options": [
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"Because under-refusal is only used in CI",
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"Because they are two opposite errors and a single number hides whichever one is worse on a given build",
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"Because over-refusal is computed by a different team",
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"Because Python dataclasses prefer multiple fields"
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],
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"correct": 1,
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"explanation": ""
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}
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]
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}
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