Few-shot Examples
Task demonstrations in the form of a small number of input-output pairs provided to a model within the prompt, used to implicitly convey the task specification and the expected output format. It is contrasted with zero-shot prompting, where no such demonstrations are supplied.
Timeline (last 90 days)
The study finds that the instruction part of the prompt (system instruction or task instruction) has a greater impact than the example part.
Few-shot examples refer to task demonstration samples provided to the model in the prompt, usually presented as input-output pairs; zero-shot prompting relies only on instructions to describe the task.
This study finds that the few-shot example part has a weaker impact than the instruction part, which is in tension with the conclusions of some high-resource-language studies.
Few-shot examples are "user question—ideal answer" pairs embedded in the prompt to demonstrate the expected answer style, format, and reasoning pattern, achieving in-context learning without fine-tuning.
Low-cost techniques similar to system prompts also include few-shot examples, output format constraints and temperature parameter tuning.
Overly long or mutually contradictory few-shot examples can interfere with the model's judgment; rules should also avoid conflicting with one another, otherwise the model will preferentially obey the last-appearing or most specific constraint.
Few-shot examples are especially effective for judgments with fuzzy category boundaries, showing the model what correct behavior looks like.
Lowering the temperature parameter, using structured outputs, and introducing few-shot examples and other deterministic measures can make outputs more predictable and reduce dependence on continuous tuning.
All Facts (8)
The study finds that the instruction part of the prompt (system instruction or task instruction) has a greater impact than the example part.
50%UnverifiedFew-shot examples refer to task demonstration samples provided to the model in the prompt, usually presented as input-output pairs; zero-shot prompting relies only on instructions to describe the task.
50%UnverifiedThis study finds that the few-shot example part has a weaker impact than the instruction part, which is in tension with the conclusions of some high-resource-language studies.
50%UnverifiedFew-shot examples are "user question—ideal answer" pairs embedded in the prompt to demonstrate the expected answer style, format, and reasoning pattern, achieving in-context learning without fine-tuning.
50%UnverifiedLow-cost techniques similar to system prompts also include few-shot examples, output format constraints and temperature parameter tuning.
50%UnverifiedOverly long or mutually contradictory few-shot examples can interfere with the model's judgment; rules should also avoid conflicting with one another, otherwise the model will preferentially obey the last-appearing or most specific constraint.
50%UnverifiedFew-shot examples are especially effective for judgments with fuzzy category boundaries, showing the model what correct behavior looks like.
50%UnverifiedLowering the temperature parameter, using structured outputs, and introducing few-shot examples and other deterministic measures can make outputs more predictable and reduce dependence on continuous tuning.
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