Kahneman-Tversky Optimization Quiz

Answers and explanations appear as you take the quiz.

1. What kind of data does KTO require?
   • Binary desirable/undesirable labels on individual outputs
   • Ranked pairs of chosen and rejected answers
   • Only unlabeled text
   • Numeric reward scores from 0 to 100
2. KTO is inspired by which body of work?
   • Einstein's relativity
   • Kahneman and Tversky's prospect theory
   • Darwin's evolution
   • Shannon's information theory
3. What is 'loss aversion' as used in KTO?
   • Avoiding any loss function entirely
   • Refusing to train on losses
   • Penalizing undesirable outputs more sharply than rewarding desirable ones
   • Making the learning rate negative
4. What kind of feedback can KTO learn from without requiring paired comparisons?
   • Only a ranked list of five outputs
   • Only a reference translation
   • Only a token-level grammar mask
   • A binary label indicating whether an individual output is desirable
5. In KTO, deviations are measured relative to what?
   • A reference point or baseline reward level
   • The total number of parameters
   • The GPU temperature
   • The vocabulary size
6. What does the KTO reference point help define?
   • The number of transformer layers
   • Whether an output’s implicit reward is above or below a baseline
   • A fixed translation in another language
   • The model’s maximum context length
7. Does KTO’s use of binary desirability feedback prove it will outperform preference-pair methods?
   • Yes, binary feedback is always superior
   • Yes, it eliminates the need to train a model
   • No; performance depends on the model, data, and evaluation setting
   • No, because binary feedback cannot be used for language models
8. Which behavioral-economics framework motivates KTO?
   • Classical signal detection theory
   • Bayesian network structure learning
   • The efficient-market hypothesis
   • Kahneman and Tversky’s prospect theory