Editing Few-Shot and Zero-Shot Learning (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Zero-Shot vs. Few-Shot vs. Full Supervision
! Approach !! Data Needed !! Accuracy !! Flexibility !! Deployment Cost
|-
| Zero-shot (CLIP/LLM) || 0 || Medium || Very high || Low (API)
|-
| Few-shot in-context || 1–10 examples || Medium-high || Very high || Low (API)
|-
| Few-shot fine-tuning || ~100 || High || Medium || Medium
|-
| Full supervision || 1000–100k || Highest || Low (task-specific) || High
|}

'''Failure modes''': Zero-shot accuracy drops dramatically for specialized/technical domains not well represented in pre-training data. Class name ambiguity — "bank" (financial institution vs. river bank) causes misclassification without context. In-context learning is sensitive to example order and formatting. Generalized zero-shot learning typically suffers from the "hubness problem" — test embeddings cluster near a few seen classes.
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