https://terms-en.ai-term-hub.com/zh/tags/reasoning/Recent content in Reasoning 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/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/reflection/Sat, 18 Jul 2026 11:32:02 +0000https://terms-en.ai-term-hub.com/zh/terms/reflection/<h2 id="definition">Definition</h2> <p>在人工智能中，反思是一种范式，模型在最终确定输出之前会暂停并评估其生成过程或输出结果。这可能涉及检查逻辑一致性、事实准确性或遵循特定约束。</p> <h3 id="summary">Summary</h3> <p>一种机制，允许AI模型分析其自身的输出、推理过程或内部状态，以改善未来的表现。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>自我修正</li> <li>思维链</li> <li>批评模型</li> <li>元认知</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>代码生成与调试</li> <li>复杂逻辑推理任务</li> <li>内容审核与安全过滤</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/self-consistency-%E8%87%AA%E6%B4%BD%E6%80%A7/">Self-consistency (自洽性)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reinforcement-learning-from-human-feedback-%E5%9F%BA%E4%BA%8E%E4%BA%BA%E7%B1%BB%E5%8F%8D%E9%A6%88%E7%9A%84%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">Reinforcement Learning from Human Feedback (基于人类反馈的强化学习)</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> </ul>https://terms-en.ai-term-hub.com/zh/terms/neuro_symbolic_ai/Sat, 18 Jul 2026 11:28:12 +0000https://terms-en.ai-term-hub.com/zh/terms/neuro_symbolic_ai/<h2 id="definition">Definition</h2> <p>神经符号人工智能将亚符号的神经学习方法与基于符号逻辑的推理系统相结合。这种混合方法旨在克服纯深度学习的局限性，例如缺乏可解释性（lack of explainability）和逻辑推理能力。</p> <h3 id="summary">Summary</h3> <p>一种结合神经网络学习能力与符号推理逻辑性和透明度的AI方法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>知识表示</li> <li>逻辑推理</li> <li>混合系统</li> <li>可解释性</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>自动定理证明</li> <li>可解释的决策支持</li> <li>机器人规划</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/symbolic-ai-%E7%AC%A6%E5%8F%B7%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/">Symbolic AI (符号人工智能)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/knowledge-graphs-%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/">Knowledge graphs (知识图谱)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/logic-programming-%E9%80%BB%E8%BE%91%E7%BC%96%E7%A8%8B/">Logic programming (逻辑编程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/interpretability-%E5%8F%AF%E8%A7%A3%E9%87%8A%E6%80%A7/">Interpretability (可解释性)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/kimi_k2/Sat, 18 Jul 2026 11:23:05 +0000https://terms-en.ai-term-hub.com/zh/terms/kimi_k2/<h2 id="definition">Definition</h2> <p>Kimi K2 代表了月之暗面大型语言模型系列的一次重大迭代。其特点在于增强了复杂逻辑推理和数学问题解决能力。</p> <h3 id="summary">Summary</h3> <p>Kimi K2 是由月之暗面（Moonshot AI）开发的大型语言模型，专为高级推理和长上下文理解而设计。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>大型语言模型</li> <li>推理</li> <li>长上下文</li> <li>月之暗面</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>复杂文档分析</li> <li>逻辑推理任务</li> <li>创意写作辅助</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <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> <li><a href="https://terms-en.ai-term-hub.com/en/terms/moonshot-ai-%E6%9C%88%E4%B9%8B%E6%9A%97%E9%9D%A2/">Moonshot AI (月之暗面)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/context-window-%E4%B8%8A%E4%B8%8B%E6%96%87%E7%AA%97%E5%8F%A3/">Context Window (上下文窗口)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/transformer-transformer%E6%9E%B6%E6%9E%84/">Transformer (Transformer架构)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/deepseek_v3/Sat, 18 Jul 2026 11:14:03 +0000https://terms-en.ai-term-hub.com/zh/terms/deepseek_v3/<h2 id="definition">Definition</h2> <p>DeepSeek V3是DeepSeek模型家族中的先进迭代版本，其特点是通过混合专家路由机制，在推理过程中仅激活少量参数子集。这种稀疏激活架构使得模型在保持强大推理能力的同时，大幅降低了计算和训练成本。</p> <h3 id="summary">Summary</h3> <p>DeepSeek V3是一款高效的混合专家（MoE）语言模型，以显著降低的训练成本实现了最先进的性能。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>混合专家模型</li> <li>稀疏激活</li> <li>训练效率</li> <li>推理能力</li> <li>混合优化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>复杂数学问题解决</li> <li>高级代码重构</li> <li>高风险逻辑推理任务</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/deepseek-deepseek/">DeepSeek (DeepSeek)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/moe-mixture-of-experts-%E6%B7%B7%E5%90%88%E4%B8%93%E5%AE%B6%E6%A8%A1%E5%9E%8B/">MoE (Mixture of Experts, 混合专家模型)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%A8%80%E7%96%8F%E6%A8%A1%E5%9E%8B-sparse-models/">稀疏模型 (Sparse Models)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/llm%E5%9F%BA%E5%87%86%E6%B5%8B%E8%AF%95-llm-benchmarking/">LLM基准测试 (LLM Benchmarking)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/datasetjackrongqwen35_reasoning_700x/Sat, 18 Jul 2026 11:13:13 +0000https://terms-en.ai-term-hub.com/zh/terms/datasetjackrongqwen35_reasoning_700x/<h2 id="definition">Definition</h2> <p>此条目指代标识符为 &lsquo;Jackrong/Qwen3.5 Reasoning 700X&rsquo; 的特定数据集仓库。它通常用于监督微调（SFT）或强化学习等场景，旨在提升模型的推理性能。</p> <h3 id="summary">Summary</h3> <p>由用户 Jackrong 在 Hugging Face 上托管的专业数据集，专为微调 Qwen 等大型语言模型以增强其复杂推理能力而精心策划。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>监督微调</li> <li>思维链</li> <li>推理增强</li> <li>Hugging Face Hub</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>针对数学或逻辑任务微调大语言模型</li> <li>基准测试推理能力的提升效果</li> <li>创建专门的推理助手</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%9B%91%E7%9D%A3%E5%BE%AE%E8%B0%83-supervised-fine-tuning/">监督微调 (Supervised Fine-Tuning)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/qwen/">Qwen</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hugging-face-datasets/">Hugging Face Datasets</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/commonsense_knowledge/Sat, 18 Jul 2026 11:10:38 +0000https://terms-en.ai-term-hub.com/zh/terms/commonsense_knowledge/<h2 id="definition">Definition</h2> <p>常识知识指的是人类自然获得的关于日常生活、物理学、社会规范和因果关系的庞大隐性信息库。在人工智能领域，获取这种知识是实现真正智能的关键挑战，因为机器往往难以像人类一样理解隐含的情境和现实世界的逻辑。</p> <h3 id="summary">Summary</h3> <p>人类拥有但机器通常缺乏的关于物理和社会世界的背景信息与直觉理解。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>隐性知识</li> <li>推理</li> <li>语境理解</li> <li>类人直觉</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>自然语言处理中的消歧</li> <li>机器人导航与交互</li> <li>问答系统</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1-knowledge-graphs/">知识图谱 (Knowledge Graphs)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%8E%A8%E7%90%86-reasoning/">推理 (Reasoning)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86-nlp/">自然语言处理 (NLP)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%85%B7%E8%BA%AB%E6%99%BA%E8%83%BD-embodied-ai/">具身智能 (Embodied AI)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/case_based_reasoning/Sat, 18 Jul 2026 11:09:42 +0000https://terms-en.ai-term-hub.com/zh/terms/case_based_reasoning/<h2 id="definition">Definition</h2> <p>CBR基于相似问题具有相似解决方案的原则运作。该过程涉及从知识库中检索最相似的历史案例，调整其解决方案以适应当前情况，并将新案例保留以供未来使用。</p> <h3 id="summary">Summary</h3> <p>基于案例的推理（CBR）是一种利用过去经验或相似案例为新问题寻找解决方案的人工智能问题解决方法。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>检索</li> <li>复用</li> <li>修正</li> <li>保留</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>法律先例分析</li> <li>医疗诊断辅助</li> <li>客户支持聊天机器人</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/knowledge-representation-%E7%9F%A5%E8%AF%86%E8%A1%A8%E7%A4%BA/">knowledge representation (知识表示)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/similarity-metrics-%E7%9B%B8%E4%BC%BC%E5%BA%A6%E5%BA%A6%E9%87%8F/">similarity metrics (相似度度量)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/expert-systems-%E4%B8%93%E5%AE%B6%E7%B3%BB%E7%BB%9F/">expert systems (专家系统)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/machine-learning-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">machine learning (机器学习)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/agentive_logic/Sat, 18 Jul 2026 11:04:26 +0000https://terms-en.ai-term-hub.com/zh/terms/agentive_logic/<h2 id="definition">Definition</h2> <p>它扩展了传统逻辑以涵盖代理性，允许系统表示信念、欲望和意图（BDI模型）。这种逻辑使智能体能够根据变化的环境动态规划行动。</p> <h3 id="summary">Summary</h3> <p>代理逻辑是指用于建模自主智能体意图、目标和决策过程的形式化推理框架。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>BDI模型</li> <li>目标导向行为</li> <li>意向性</li> <li>不确定性下的推理</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>设计自主机器人</li> <li>创建智能助手</li> <li>模拟社会互动</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bdi_architecture-bdi%E6%9E%B6%E6%9E%84/">bdi_architecture (BDI架构)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/planning_algorithms-%E8%A7%84%E5%88%92%E7%AE%97%E6%B3%95/">planning_algorithms (规划算法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/intentional_stance-%E6%84%8F%E5%90%91%E7%AB%8B%E5%9C%BA/">intentional_stance (意向立场)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/autonomous_agents-%E8%87%AA%E4%B8%BB%E6%99%BA%E8%83%BD%E4%BD%93/">autonomous_agents (自主智能体)</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/chain_of_thought_prompting/Sat, 18 Jul 2026 10:59:27 +0000https://terms-en.ai-term-hub.com/zh/terms/chain_of_thought_prompting/<h2 id="definition">Definition</h2> <p>思维链（CoT）提示通过明确要求模型阐述其逐步逻辑，从而提升大型语言模型在复杂推理任务上的表现。与直接跳跃到最终答案不同，CoT引导模型展示其思考过程，这有助于解决需要多步逻辑推导的问题，如数学应用题或复杂的逻辑推理任务。</p> <h3 id="summary">Summary</h3> <p>思维链提示是一种鼓励大语言模型在生成最终答案之前，先生成中间推理步骤的技术。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>逐步推理</li> <li>少样本学习</li> <li>逻辑演绎</li> <li>中间步骤</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>解决数学应用题</li> <li>复杂逻辑推理任务</li> <li>调试代码生成错误</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;Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. If each can has 3 balls, how many does he have?</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">A: Roger started with 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. The answer is 11.&#34;</span> </span></span><span style="display:flex;"><span>print(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/zero-shot-prompting-%E9%9B%B6%E6%A0%B7%E6%9C%AC%E6%8F%90%E7%A4%BA/">Zero-Shot Prompting (零样本提示)</a></li> <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/self-consistency-%E8%87%AA%E6%B4%BD%E6%80%A7/">Self-Consistency (自洽性)</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/multi_step/Sat, 18 Jul 2026 10:56:59 +0000https://terms-en.ai-term-hub.com/zh/terms/multi_step/<h2 id="definition">Definition</h2> <p>多步方法涉及将复杂的查询或任务分解为更小的、可执行的步骤。这种方法在推理任务中至关重要，例如数学问题解决或代码生成。</p> <h3 id="summary">Summary</h3> <p>一种需要执行一系列逻辑运算或计算才能得出最终解决方案的问题解决策略。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>推理</li> <li>中间步骤</li> <li>思维链</li> <li>分解</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>复杂数学应用题</li> <li>具有依赖关系的代码生成</li> <li>逻辑演绎任务</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/step-by-step-%E9%80%90%E6%AD%A5/">step-by-step (逐步)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/algorithmic-thinking-%E7%AE%97%E6%B3%95%E6%80%9D%E7%BB%B4/">algorithmic thinking (算法思维)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/decomposition-%E5%88%86%E8%A7%A3/">decomposition (分解)</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>