如何生成文本：在 Transformers 中使用不同的解碼方法進行語言生成
Original: How to generate text: using different decoding methods for language generation with Transformers
This classic technical blog post written by Hugging Face takes an in-depth look at how to select and tune different "decoding methods" when…
這篇 Hugging Face 的經典技術指南介紹了自迴歸語言模型中不同的文本生成解碼策略。內容涵蓋傳統的貪婪搜索（Greedy Search）與束搜索（Beam Search），並探討如何透過隨機採樣（Sampling）、溫度調節（Temperature）、Top-K 與 Top-p（核採樣）來解決生成文本重複或單調的問題。這對於想優化 LLM 輸出品質的開發者與研究者來說是必讀的基礎教學。
This classic technical blog post written by Hugging Face takes an in-depth look at how to select and tune different "decoding methods" when performing autoregressive language generation using the Transformers library. Decoding methods determine how the model selects the next token based on the probability distribution predicted from prior context, and they have a decisive impact on the quality, diversity, and coherence of generated text.
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