Kiini na Maswali ya Sampuli ya Juu-k

Majibu na maelezo huonekana unapojibu maswali.

1. Sampuli za top-k huzuia vipi chaguo za tokeni?
   • Huweka tokeni za k pekee zenye uwezekano mkubwa zaidi, kisha sampuli miongoni mwazo
   • Huweka tokeni hadi uwezekano wa limbikizo uzidi k
   • Daima huchagua ishara moja inayowezekana zaidi
   • Huondoa tokeni zenye uwezekano mdogo wa k pekee
2. Ni nini hufafanua 'kiini' katika sampuli ya kiini (juu-p)?
   • K maneno ya mara kwa mara katika data ya mafunzo
   • Seti ndogo zaidi ya tokeni ambazo uwezekano wake unafikia angalau uk
   • Tokeni moja ya uwezekano mkubwa zaidi
   • Tokeni zote zenye uwezekano wa zaidi ya 50%
3. Je, ni faida gani kuu ya top-p juu ya top-k?
   • Daima ni haraka kuhesabu
   • Kamwe haitoi pato la nasibu
   • Idadi ya tokeni za wagombea hubadilika kulingana na imani ya kielelezo
   • Inapuuza uwezekano wa ishara
4. Je, kigezo cha halijoto hufanya nini kabla ya sampuli?
   • Inaweka upeo wa urefu wa mlolongo
   • Inachagua lugha ya pato
   • Inahesabu ukubwa wa msamiati
   • Hupunguza usambazaji wa uwezekano kuwa mkali zaidi au laini
5. What result did the nucleus-sampling paper report for its tested open-ended generation tasks?
   • It improved diversity without sacrificing fluency and coherence in the reported comparisons
   • It guaranteed factual correctness in every output
   • It eliminated all sampling randomness
   • It improved translation quality on every benchmark
6. For a greedy decoding baseline with no token sampling, how should generation be configured?
   • Disable sampling and use greedy decoding to select the highest-probability token at each step
   • Use high temperature and a large nucleus threshold
   • Randomize the temperature at every step
   • Keep sampling enabled but increase top-p
7. After top-k truncation removes tokens, what happens to the remaining sampling probabilities?
   • They remain assigned their original total mass below one
   • They are replaced with equal probabilities
   • They are renormalized over the retained top-k tokens
   • They are converted into beam-search scores
8. What did Holtzman et al. report about likelihood-maximizing decoding for open-ended text?
   • It always yields the most diverse human-like passages
   • Huondoa hitaji la muundo wa lugha
   • It guarantees factual correctness
   • It can produce bland and repetitive text despite strong likelihood scores