Mirayiridzo Tuning Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Pasina kurairirwa, modhi yakasvibira ingapindura sei kukurumidza 'Chii chinonzi guta guru reFrance?'
   • Zvinogara zvichiramba kupindura
   • Inodzosera kukanganisa
   • Inogona kuenderera mberi nemimwe mibvunzo kana zvinyorwa zvine hukama pane kupindura
   • Inopindura zvakakwana nguva dzose
2. Tuning yekuraira inotsanangurwa zvakanyanya se:
   • Kusimbisa kudzidza kubva kumhinduro dzevanhu
   • Kudzidzira padandemutande zvinyorwa
   • Kugadzirisa tembiricha parameter
   • Inotariswa kurongedza zvakanaka pamirairo-uye-mhinduro pairs
3. How does supervised instruction tuning differ from reward-model-based RLHF in the published InstructGPT recipe?
   • It is exactly the same optimization stage
   • It trains against reference responses, while the later preference stages use comparisons and a reward model
   • It requires no training examples
   • It only changes a sampling temperature
4. Why do instruction-tuning researchers evaluate models on held-out tasks and instructions?
   • To assess generalization beyond the examples used for training
   • To prove that every unseen task will succeed
   • To avoid checking any generated responses
   • To guarantee the same accuracy for every task mixture
5. Ndeipi yakajairika kurongeka kwematanho ekudzidziswa kweazvino mubatsiri wekutaura?
   • RLHF, wozofanodzidzira, wozoteedzera kuraira
   • Mirayiridzo tuning, ipapo pretraining
   • Kungodzidzira chete, hapana chimwe
   • Pretraining, wobva waraira tuning, wozosarudza RLHF
6. What does LIMA's curated-data experiment support as a research conclusion?
   • Exactly 1,000 examples are enough for every model and task
   • More data can never help any instruction-tuned model
   • Carefully selected small datasets can be effective in an evaluated configuration
   • Only synthetic examples can train an assistant
7. Which training stage did the original LIMA experiment omit while using its 1,000 curated prompt-response pairs?
   • Reinforcement learning and human preference modeling
   • Supervised response loss
   • A pretrained base model
   • Prompt-response examples
8. A tuned model follows the requested answer format. What does that behavior alone fail to establish?
   • That an output format was requested
   • That every factual statement in the answer is correct
   • That the model produced a response
   • That the response can be inspected