alleryStart learning →The central finding is that
AFew-shot ICL beats many-shot whenever shots exceed 16
BMany-shot only helps when full fine-tuning is also done
CMany-shot performance is flat regardless of shot count
DMany-shot ICL beats few-shot on a wide range of tasks
Answer & Solution
Correct answer: D. Many-shot ICL beats few-shot on a wide range of tasks
Many-shot (hundreds to thousands of shots) consistently outperforms few-shot across translation, summarization, planning, reasoning, and reward modelling.
The gap is largest on non-natural-language tasks.
Keep practising Many Shot in Context Learning
Browse all Many Shot in Context Learning questions for Claude, or explore more Aifoundations topics.
Related questions
Rule on these many shot claimsThe implication for prompt design when shots are abundant isLong-context scaling laws (next-token loss)Many-shot ICL can overcomeMany-shot ICL can perform comparably toThe order of examples in many-shot promptsAbstractive summarization on XSumMachine translation many-shot results