From f6c6c58e6faad4089d1a9966d6e7f8f3ef78f12f Mon Sep 17 00:00:00 2001 From: sheli Date: Sat, 9 Dec 2023 18:56:39 +0800 Subject: [PATCH] docs: update reasoning lats --- .../public/assets/images/reasoning/lats1.png | Bin 0 -> 89461 bytes .../public/assets/images/reasoning/lats2.jpg | Bin 0 -> 104342 bytes .../public/assets/images/reasoning/lats3.jpg | Bin 0 -> 61274 bytes .../public/assets/images/reasoning/lats4.jpg | Bin 0 -> 94810 bytes .../public/assets/images/reasoning/lats5.jpg | Bin 0 -> 114980 bytes src/zh/posts/reasoning/LATS.md | 86 ++++++++++++++++++ 6 files changed, 86 insertions(+) create mode 100644 src/.vuepress/public/assets/images/reasoning/lats1.png create mode 100644 src/.vuepress/public/assets/images/reasoning/lats2.jpg create mode 100644 src/.vuepress/public/assets/images/reasoning/lats3.jpg create mode 100644 src/.vuepress/public/assets/images/reasoning/lats4.jpg create mode 100644 src/.vuepress/public/assets/images/reasoning/lats5.jpg create mode 100644 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ToT和RAP仅依赖于语言模型的内部知识进行状态扩展和评估。而LATS引入了环境(Environment)的反馈。 + +2. 通过与外部环境的交互,LATS可以获得额外的观察和奖励信号,这些信号指导树搜索过程,并可用于增强语言模型的感知能力,避免事实hallucination(事实幻觉)。 + +3. ToT和RAP使用简单的搜索算法(BFS、DFS等),而LATS采用了更具原则性的蒙特卡洛树搜索,可以更好地平衡exploration和exploitation。 + +4. LATS还引入了自我反思(Self-Reflection)模块,语言模型可以从反思和经验中学习,不需要额外的训练优化。这种自我完善的能力也是ToT和RAP所没有的。 + +5. 综上,LATS是一个更为完整和统一的框架,它协同了规划、推理和行动三者的能力,可以更好地把语言模型作为决策代理开发和应用到复杂任务中。 + +## 3 统一规划、推理和行动 + +### 3.1 大预言模型智能体(LLM Agent) + +LATS是基于ReAct的支持顺序推理或决策任务的模型。该模型使用LM作为基础决策者,并通过采样多个动作来构建最佳轨迹。这种方法可以在决策和推理空间中进行更大的探索。 + +### 3.2 LATS + +![](/assets/images/reasoning/lats3.jpg "图3.1 LATS的工作流程") + +1. Selection(选择):使用UCT算法基于价值和访客次数选择树中最适合扩展的节点。从根节点开始,在每一层上选择UCT值最高的子节点,直到达到叶节点。 +2. Expansion(扩展):在选定要扩展的叶子节点上,使用语言模型pθ根据当前状态采样n个可能的操作。每个操作会影响环境并返回一个观察结果。这就添加了n个新的子节点到树中。 +3. Evaluation(评估):使用语言模型pV对新增的每个子节点进行标量评分,反映解决方案的进展程度。这些评分用作启发式信息来指导搜索算法。 +4. Simulation(模拟):从当前选择的节点开始扩展,采样高评分的节点直到达到终止节点,为整个解决方案轨迹提供反馈。 +5. Backpropagation(反向传播):使用模拟结果更新沿途所有节点的评分,为后续选择提供更好的估计。 +6. Reflection(反思):如果轨迹失败,使用语言模型p_{ref}生成文本反思,总结错误并提出改进建议。这些反思存储并用于增强代理。 +这些操作循环执行,直到完成任务或达到计算预算。LATS通过搜索、环境交互和反思协同工作,以增强语言模型的推理和决策能力。 + +## 4 实验 + +本文介绍了LATS方法在不同决策领域的应用。这些领域包括编程、HotPotQA和WebShop。LATS方法结合了推理和行动能力,具有广泛的适用性。 + +![](/assets/images/reasoning/lats4.jpg "图4.1 编程领域实验结果") + +本文研究了在编程任务中使用外部观察的重要性,通过在Humaneval和MBPP数据集上评估基线和LATS的表现,发现搜索和语义反馈对于更好的性能至关重要。LATS在两个数据集上的表现最好,RAP使用类似的搜索算法,也表明了外部反馈对于困难的推理任务(如编程)的重要性。使用LATS和GPT-4在HumanEval数据集上取得了最好的表现。 + +![](/assets/images/reasoning/lats5.jpg "图4.2 HotPotQA和WebShop实验结果") + +本文研究了在HotPotQA数据集上使用内部推理和外部检索策略的表现。现代LLMs已经编码了事实知识,可以直接回答问题。搜索方法ToT和RAP可以采样和探索更多输出,对需要推理的问题有更大的提升。LATS比ReAct表现更好,即使采样相同数量的轨迹,通过扩展更多节点进行有原则的搜索。在内部推理方面,LATS与RAP相当,但表现不如行动。将内部和外部推理结合起来,LATS的表现最好,表明外部反馈在增强推理方面的重要性。 + +本文介绍了一个复杂的决策环境WebShop,它是一个在线购物环境,由一个包含1.18M真实产品和12k人类指令的网站组成。文章使用预构建的搜索和点击命令以及浏览器反馈和反思作为观察。通过平均得分和成功率两个指标来评估性能。结果表明,GPT-3.5与ReAct在WebShop中表现出与模仿学习相当的竞争力,并且可以超越强提示策略的强化学习技术。使用LATS可以显著提高性能,表明它可以在相同的迭代次数下实现更有效的探索。 \ No newline at end of file