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Generative AI glossary: Difference between revisions
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* | * Pretraining (預訓練)：「在機器學習模型正式針對特定任務進行微調之前，先以大規模、通用型資料集進行初始訓練的過程。」(moveworks, n.d.<ref>[https://www.moveworks.com/us/en/resources/ai-terms-glossary/pre-training What is Pre-Training?]</ref><ref>[https://www.databricks.com/blog/llm-pre-training-and-custom-llms Understanding LLM Pre-Training and Custom LLMs | Databricks Blog]</ref>) | ||
* prompt engineering (提示工程)：「提示工程」是反覆迭代的過程，透過撰寫提示詞 (prompt) 並評估模型的生成結果，來獲得您想要的結果。寫結構良好的提示是確保大型語言模型提供準確、高品質的關鍵。以下是幾種可用於最佳化生成結果的常見技巧：(1) 零樣本提示（Zero-shot prompting）：在不提供任何範例的情況下提示詞，完全依賴模型既有的知識。(2) 單一樣本提示（One-shot prompting）：在提示詞中提供一個範例，引導模型的回覆方向。(3) 少數樣本提示（Few-shot prompting）：在提示詞中提供多個範例，以符合您要求的模式或任務。」(Google, n.d.<ref>[https://docs.cloud.google.com/docs/generative-ai/glossary Generative AI glossary | Google Cloud Documentation]</ref><ref>[https://medium.com/@planetoid/%E7%99%BD%E8%A9%B1%E9%80%9A%E4%BF%97%E8%A7%A3%E9%87%8B-zero-shot-one-shot-few-shot-learning-68c56eca12ae 白話通俗解釋 zero-shot, one-shot, few-shot learning]</ref>) | |||
== S == | == S == | ||