Quiz połączony z przewodnikiem · Średni Poziom

Quiz dotyczący wzorcowego zanieczyszczenia

Testuje wyciek elementów testu porównawczego do danych szkoleniowych, metody wykrywania, takie jak nakładanie się n-gramów i prawdopodobieństwo Min-K%, oraz środki łagodzące, takie jak kanarki i podziały czasu.

Powiązane ścieżki prowadząceZanieczyszczenie wzorcowe
Pytanie 1 z 8

Co to jest zanieczyszczenie wzorca?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. Co to jest zanieczyszczenie wzorca?
    • When a benchmark contains incorrect answer keys
    • When a model is tested on too few questions
    • When benchmark test items or their answers appear in a model's training data
    • When two benchmarks measure the same skill
  2. Jaki jest cel kanarkowego sznurka zawartego w zadaniach BIG-bench?
    • To let data teams filter those documents from training data and test whether a model saw them
    • To encrypt the answers
    • To mark which tasks are the hardest
    • To identify which model generated each answer
  3. Co sugeruje badanie GSM1k?
    • All models scored higher on the new problems
    • GSM8K has no errors
    • Contamination cannot be measured without training data
    • Some models scored noticeably lower on fresh, matched problems than on GSM8K
  4. Jaką analizę nakładania się przeprowadzono w artykule GPT-3?
    • Embedding similarity on full documents
    • 13-gram overlap between benchmarks and training data
    • Human review of every training document
    • Min-K% Prob on each benchmark
  5. Jaka jest podstawowa idea Min-K% Prob?
    • Removing the least common k percent of words from training data
    • Training a second model to classify contaminated text
    • Text the model has seen tends to have few very low-probability tokens
    • Measuring the length of the model's answers
  6. How does a time-based split, as in LiveCodeBench, help detect contamination?
    • It compares performance on problems published before and after the model's training cutoff
    • It measures how long the model takes to answer
    • It splits the model into older and newer layers
    • It only counts problems answered within a time limit
  7. Why does finding no exact overlap not prove a benchmark is clean?
    • Exact matching is too slow to run
    • Benchmarks change their answers over time
    • Models never memorize exact text
    • Paraphrased or translated copies can slip past string matching
  8. In n-gram decontamination, what happens if n is chosen too small?
    • Near-copies with small edits are missed
    • Many false positives from common phrases are flagged
    • The filter stops working entirely
    • Only answers, not questions, are matched