https://terms-en.ai-term-hub.com/zh/tags/prompting/Recent content in Prompting on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/zero_shot_prompting/Sat, 18 Jul 2026 11:38:27 +0000https://terms-en.ai-term-hub.com/zh/terms/zero_shot_prompting/<h2 id="definition">Definition</h2> <p>零样本提示涉及直接通过文本提示要求预训练语言模型完成任务，而不提供任何少样本示例或进行额外的微调。该模型利用其在大规模数据上学习到的通用知识和指令遵循能力来生成响应，</p> <h3 id="summary">Summary</h3> <p>一种技术，大型语言模型无需先前示例或微调，仅依靠自然语言指令即可执行任务。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>提示工程</li> <li>涌现能力</li> <li>上下文学习</li> <li>指令微调</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>AI 应用的快速原型设计</li> <li>动态任务切换</li> <li>降低数据标注成本</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/few-shot-prompting-%E5%B0%91%E6%A0%B7%E6%9C%AC%E6%8F%90%E7%A4%BA/">Few-Shot Prompting (少样本提示)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/chain-of-thought-%E6%80%9D%E7%BB%B4%E9%93%BE/">Chain-of-Thought (思维链)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/fine-tuning-%E5%BE%AE%E8%B0%83/">Fine-Tuning (微调)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/llm-%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">LLM (大型语言模型)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/tree_of_thoughts/Sat, 18 Jul 2026 11:37:15 +0000https://terms-en.ai-term-hub.com/zh/terms/tree_of_thoughts/<h2 id="definition">Definition</h2> <p>思维树（ToT）通过允许模型在每个步骤中探索多个不同的推理路径并形成树状结构，扩展了传统的思维链提示方法。模型会对这些路径进行评估，以决定后续行动。</p> <h3 id="summary">Summary</h3> <p>思维树是一种推理框架，它同时探索多种可能的推理路径，并通过评估这些路径来选择最有希望的下一步。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>搜索算法</li> <li>回溯</li> <li>状态评估</li> <li>推理路径</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>复杂数学问题求解</li> <li>创意写作生成</li> <li>策略性游戏对弈</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/chain-of-thought-%E6%80%9D%E7%BB%B4%E9%93%BE/">Chain of Thought (思维链)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/beam-search-%E6%9D%9F%E6%90%9C%E7%B4%A2/">Beam Search (束搜索)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reinforcement-learning-%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">Reinforcement Learning (强化学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reasoning-%E6%8E%A8%E7%90%86/">Reasoning (推理)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/reasoning_model/Sat, 18 Jul 2026 11:31:50 +0000https://terms-en.ai-term-hub.com/zh/terms/reasoning_model/<h2 id="definition">Definition</h2> <p>与专注于流畅性的标准生成模型不同，推理模型优先考虑数学、编码和逻辑谜题等多步骤任务的准确性。它们通常采用思维链 (Chain-of-Thought) 等技术来增强逻辑推理能力。</p> <h3 id="summary">Summary</h3> <p>专门优化用于执行复杂逻辑演绎、逐步问题解决和思维链处理的 AI 模型。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>思维链 (Chain-of-Thought)</li> <li>逻辑演绎</li> <li>逐步推理</li> <li>复杂问题解决</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>数学证明生成</li> <li>复杂代码调试</li> <li>战略规划辅助</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%80%9D%E7%BB%B4%E9%93%BE-chain-of-thought/">思维链 (Chain-of-Thought)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%B3%BB%E7%BB%9F-2-%E6%80%9D%E7%BB%B4-system-2-thinking/">系统 2 思维 (System 2 thinking)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%80%BB%E8%BE%91%E7%BC%96%E7%A8%8B-logic-programming/">逻辑编程 (Logic programming)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/cot-%E6%8F%90%E7%A4%BA-cot-prompting/">CoT 提示 (CoT prompting)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/react/Sat, 18 Jul 2026 11:01:41 +0000https://terms-en.ai-term-hub.com/zh/terms/react/<h2 id="definition">Definition</h2> <p>ReAct 框架使大语言模型能够以交错的方式生成推理轨迹和特定于任务的行动。通过模拟类人的思维过程，它允许模型与外部环境进行交互（原文截断，意为 interaction with external environment）。</p> <h3 id="summary">Summary</h3> <p>ReAct 是一种提示范式，通过结合推理和行动来提升大型语言模型在复杂任务上的表现。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>交错推理</li> <li>行动生成</li> <li>观察整合</li> <li>思维链</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>复杂问答系统</li> <li>自主智能体</li> <li>事实核查工作流</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/chain-of-thought-%E6%80%9D%E7%BB%B4%E9%93%BE/">Chain-of-Thought (思维链)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/agent-%E6%99%BA%E8%83%BD%E4%BD%93/">Agent (智能体)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/prompt-engineering-%E6%8F%90%E7%A4%BA%E5%B7%A5%E7%A8%8B/">Prompt Engineering (提示工程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/llm-%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">LLM (大型语言模型)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/few_shot_prompting/Sat, 18 Jul 2026 11:00:03 +0000https://terms-en.ai-term-hub.com/zh/terms/few_shot_prompting/<h2 id="definition">Definition</h2> <p>这种方法利用大型语言模型的上下文学习能力，直接在提示词中提供几个说明性示例。与需要更新模型权重的微调不同，少样本提示通过展示期望的输出格式或逻辑，让模型在不重新训练的情况下适应新任务。</p> <h3 id="summary">Summary</h3> <p>少样本提示是一种技术，通过在提示词中提供少量输入-输出示例来引导大型语言模型的行为。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>上下文学习</li> <li>提示工程</li> <li>基于示例的引导</li> <li>零样本对比</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>情感分析格式化</li> <li>代码风格适配</li> <li>结构化数据提取</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span>response <span style="color:#f92672">=</span> llm<span style="color:#f92672">.</span>generate( </span></span><span style="display:flex;"><span> prompt<span style="color:#f92672">=</span><span style="color:#e6db74">&#34;Translate English to French:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> </span></span><span style="display:flex;"><span> <span style="color:#e6db74">&#34;Hello -&gt; Bonjour</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> </span></span><span style="display:flex;"><span> <span style="color:#e6db74">&#34;World -&gt; Monde</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> </span></span><span style="display:flex;"><span> <span style="color:#e6db74">&#34;Cat -&gt; ?&#34;</span> </span></span><span style="display:flex;"><span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/zero_shot-%E9%9B%B6%E6%A0%B7%E6%9C%AC/">zero_shot (零样本)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/prompt_engineering-%E6%8F%90%E7%A4%BA%E5%B7%A5%E7%A8%8B/">prompt_engineering (提示工程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/in_context_learning-%E4%B8%8A%E4%B8%8B%E6%96%87%E5%AD%A6%E4%B9%A0/">in_context_learning (上下文学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/llm-%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">llm (大型语言模型)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/in_context_learning/Sat, 18 Jul 2026 07:44:46 +0000https://terms-en.ai-term-hub.com/zh/terms/in_context_learning/<h2 id="definition">Definition</h2> <p>上下文学习（ICL）允许大型语言模型在不更新权重的情况下适应新任务。通过在提示上下文中提供输入-输出对，模型可以推断出模式并执行相应任务。</p> <h3 id="summary">Summary</h3> <p>一种模型通过观察提示中提供的示例来执行任务的技术。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>少样本学习</li> <li>零样本</li> <li>提示设计</li> <li>无权重适配</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>快速测试模型在新数据集上的能力</li> <li>对话代理中的动态任务切换</li> <li>无需重新训练即可原型化AI应用</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span>prompt <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Translate English to French:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">English: Hello</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">French: Bonjour</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">English: Cat</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">French:&#34;</span> </span></span><span style="display:flex;"><span>response <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>generate(prompt) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%8F%90%E7%A4%BA%E5%B7%A5%E7%A8%8B/">提示工程</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%B0%91%E6%A0%B7%E6%9C%AC/">少样本</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E9%9B%B6%E6%A0%B7%E6%9C%AC/">零样本</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%85%83%E5%AD%A6%E4%B9%A0/">元学习</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/chain_of_thought/Sat, 18 Jul 2026 07:44:10 +0000https://terms-en.ai-term-hub.com/zh/terms/chain_of_thought/<h2 id="definition">Definition</h2> <p>思维链（Chain-of-Thought, CoT）提示是一种策略，引导大型语言模型在得出最终答案之前，逐步生成推理解释。通过将复杂问题分解为中间步骤，这种方法显著提高了模型在处理逻辑推理和数学问题时的准确性和可解释性。</p> <h3 id="summary">Summary</h3> <p>一种提示技术，鼓励大型语言模型在回答之前生成中间推理步骤。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>提示工程</li> <li>推理步骤</li> <li>可解释性</li> <li>零样本思维链</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>解决复杂的数学应用题</li> <li>逻辑演绎任务</li> <li>调试代码片段</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/prompt-engineering-%E6%8F%90%E7%A4%BA%E5%B7%A5%E7%A8%8B/">Prompt Engineering (提示工程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/large-language-models-%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">Large Language Models (大型语言模型)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/few-shot-learning-%E5%B0%91%E6%A0%B7%E6%9C%AC%E5%AD%A6%E4%B9%A0/">Few-Shot Learning (少样本学习)</a></li> </ul>