Verified80% confidenceFactTime unknown
Few-Shot Learning refers to providing a small number of examples (typically 2–8) in the prompt, allowing the model to complete new tasks through analogical reasoning.
15
Sources
80%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen a Skill is presented in the form of a few-shot example set, it essentially leverages the model's In-Context Learning capability without modifying the model weights.80% similarVerifiedFew-shot prompting activates in-context learning capabilities by providing examples.80% similarUnverifiedThe goal of one-shot learning is to enable a model to understand and reproduce a task from just a single demonstration or example.77% similarUnverifiedZero-shot Learning refers to a model's ability to complete new tasks solely based on general knowledge accumulated during the pre-training phase, without having been trained on those specific tasks.76% similarUnverifiedFew-shot Learning相比全量微调的优势在于零训练成本和即时切换能力，只需更换几个示例即可调整品牌语气76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/19978API
curl https://kongchang.com/api/v1/knowledge/claims/19978MCP
get_claim(id=19978)