Maswali yanayounganishwa na mwongozo · Ngumu Kiwango

Maswali ya Vigezo vya AI

Angalia jinsi kazi za alama, data, mipangilio, kutokuwa na uhakika, uchafuzi, na majaribio mahususi ya programu hutengeneza kile alama ya AI inaweza kukuambia.

Njia za mwongozo zinazohusianaVigezo vya AI
Swali 1 ya 8

What is an AI benchmark?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. What is an AI benchmark?
    • A defined set of tasks, data, and scoring rules used to compare systems
    • A universal measure of intelligence
    • A guarantee the highest-scoring model is best for an application
    • A list of model parameters
  2. Two systems both report “accuracy.” What should be checked before comparing them?
    • Only model names
    • Only the number of parameters
    • Task definition, examples, scoring rules, and test conditions
    • Only whether the two reports use the same metric name
  3. Why record model version, prompt, tools, attempts, and evaluation date?
    • A record guarantees no data leakage
    • Those settings can change benchmark results
    • Settings do not affect model behavior
    • Only the date matters
  4. What is dataset contamination in an evaluation?
    • The benchmark includes multiple task types
    • A model uses a different prompt
    • A dataset contains timestamps
    • Test items or close variants were available during development
  5. A 100-question test shows one model answers 81 correctly and another 83. What does that difference alone establish?
    • Only the observed counts, not a reliable advantage by itself
    • That the second model is universally better
    • That the first model cannot solve the task
    • That the test set was contaminated
  6. Why can an average benchmark score hide weaknesses?
    • Averages always reveal every failure
    • A single metric measures all applications equally
    • Performance may differ across tasks, conditions, or groups
    • Scores do not depend on the task mix
  7. What should be compared alongside task success?
    • Only parameter count
    • Cost and latency
    • Only leaderboard position
    • Only how many times the result was cited
  8. Why add a private evaluation set for an application?
    • It guarantees a higher public score
    • It eliminates the need to document prompts
    • It prevents all model errors
    • It tests whether public-benchmark results transfer to the intended workflow