Unverified50% confidenceSolutionExact time
Adding output examples (few-shot examples) is an effective means of reducing hallucinations and controlling format, as the model learns the expected output pattern from the examples.
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9/12/2026
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UnverifiedFew-shot prompting leverages the in-context learning capability of the Transformer architecture, allowing the model to infer the expected output format from in-context examples alone without updating its parameters.74% similarUnverifiedIn prompt engineering, few-shot examples guide output format by providing 2–5 examples, while Chain-of-Thought requires the model to "think step by step" to activate reasoning capability.74% similarUnverified自动微分的前向模式适合输入少输出多的场景，反向模式适合输入多输出少的场景，深度学习因参数多而损失为标量属于后者74% similarUnverified少样本学习通过在Prompt中提供2-5个示例帮助模型快速理解输出格式和风格要求72% similarPartially VerifiedFew-shot prompting involves providing several input-output examples in the prompt to allow the model to learn by analogy.71% similar
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