From 337b363cbd57c60e31257f6e2c232ae34c8869ef Mon Sep 17 00:00:00 2001 From: sheli00 <44807582+sheli00@users.noreply.github.com> Date: Mon, 13 Nov 2023 18:41:07 +0800 Subject: [PATCH] add xot (#173) --- .../public/assets/images/reasoning/xot1.png | Bin 0 -> 147758 bytes .../public/assets/images/reasoning/xot2.png | Bin 0 -> 80274 bytes .../public/assets/images/reasoning/xot3.png | Bin 0 -> 60587 bytes src/zh/posts/reasoning/XoT.md | 90 ++++++++++++++++++ 4 files changed, 90 insertions(+) create mode 100644 src/.vuepress/public/assets/images/reasoning/xot1.png create mode 100644 src/.vuepress/public/assets/images/reasoning/xot2.png create mode 100644 src/.vuepress/public/assets/images/reasoning/xot3.png create mode 100644 src/zh/posts/reasoning/XoT.md diff --git a/src/.vuepress/public/assets/images/reasoning/xot1.png b/src/.vuepress/public/assets/images/reasoning/xot1.png new file mode 100644 index 0000000000000000000000000000000000000000..12560b75dc22049a44d243ef23e17d62b37c48a5 GIT 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b/src/zh/posts/reasoning/XoT.md @@ -0,0 +1,90 @@ +--- +author: 猞猁-zlj +icon: wand-magic-sparkles +date: 2023-11-13 +shortTitle: "XoT: 强化学习增强思维生成" +category: + - 大模型推理 +tag: + - 推理 + - LLM + - CoT + - 强化学习 +--- + +# XoT: 强化学习增强思维生成 + +该文介绍了一种名为`XOT`的提示技术,它增强了像GPT-3和GPT-4这样的大型语言模型(llm)解决复杂问题的潜力。 + + + +::: tip +论文题目:Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation +作者:Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, et al. +机构:Microsoft +::: + + + +--- + +## 1 当前提示技术的局限性 + +LLM的最新进展通过将复杂的问题分解成更易于管理的“思想”,然后通过自然语言提示表达出来,从而实现了解决问题的新方法。但是大多数现有的提示技术都有局限性。 + +![](/assets/images/reasoning/xot1.png "图1.1 XoT和其他方法示意图") + +- **IO**: 输入-输出(IO)提示仅适用于具有单步解决方案的简单问题,缺乏灵活性。 +- **CoT**: 思维链(CoT)能够逐步解决问题,但仅限于线性思维结构,限制了灵活性。 +- **ToT和GoT**: 允许更灵活的思维结构,如树或图。但是它们需要LLM本身来评估中间思想,通过多个LLM调用产生大量的计算成本。 + +从本质上讲,当前的提示技术面临着`“彭罗斯三角”`约束——它们最多可以实现两个属性(**性能、效率、灵活性**),但不能同时实现这三个属性。 + +## 2 XoT的组成 + +XOT集成了`强化学习`和`蒙特卡罗树搜索(MCTS)`,将外部知识注入提示过程。这增强了llm的功能,并同时实现了更高的性能、效率和灵活性。 + +### 2.1 MCTS模块 + +MCTS模块在特定任务上进行预训练,以学习有关有效思维搜索的领域知识。轻量级策略和价值网络指导搜索。 + +MCTS大概可以被分成四步。选择(Selection),拓展(Expansion),模(Simulation),反向传播(Backpropagation)。 + +![](/assets/images/reasoning/xot2.png "图2.1 蒙特卡罗树搜索") + +选择:算法从根节点开始,在当前状态下从可用的单步思想生成集中选择一个动作。这个过程一直持续到到达当前树中的一个叶节点。选择以PUCT算法为指导,目标是最大化上置信度界(UCB)。 + +评估和扩展:在到达先前未选择的叶节点时,会扩展到下一步进行新思想探索的状态。这种展开涉及到对状态的值和动作概率的评估,用θ参数化的神经网络建模,即(Pθ(s), vθ(s)) = fθ(s)。其中Pθ(s)是s上所有动作的先验概率,vθ(s)表示其预测状态值。这两个值被保留和存储用于备份,状态被标记为“已访问”。 + +反向传播:随着叶子节点在上述阶段的扩展(可以是未探索状态,也可以是终端状态),算法继续通过反向传播更新所有Q(s, a)值。对于未探索的节点,这种更新涉及计算其估计值vθ的平均值,而对于终止的节点,它是基于真实奖励r。这些更新发生在信息沿着轨迹反向传播到后续节点时。这里每个状态-操作对的访问计数也会增加。 + + +### 2.2 LLM求解器 + +利用LLM的内部知识对MCTS的思想进行提炼和修正。这种协作过程提高了“思维”质量。 + +在思想搜索过程中,预训练的MCTS利用策略网络和价值网络有效地探索搜索空间并生成思想轨迹。这包括迭代地选择、展开、计算和反向传播节点。思想轨迹提供给LLM作为提示。 + +LLM利用其内部知识来检测思想中的任何错误。如果发现错误,则使用MCTS模块通过额外的模拟来修改思想。 + +这个过程不断重复,直到LLM使用修改后的高质量思想解决问题。 + +## 3 实验结果 + +![](/assets/images/reasoning/xot3.png "图3.1 实验结果") + +研究人员对《Game of 24》、《8-Puzzle》和《Pocket Cube》等需要长期规划的复杂任务进行了XOT评估: + +在所有任务中,XOT的准确率明显优于IO、CoT、ToT和GoT等基线。 + +经过思想修正,XOT在Game of 24中仅使用1-2个LLM调用就实现了高达90%的准确率,显示出高效率。 + +XOT高效地为问题生成多种不同的解决方案,展示了灵活性。 + +对于像8-Puzzle和Pocket Cube这样的空间推理任务,XOT使llm能够解决他们以前遇到的问题。 + +这些结果突出了XOT如何通过有效和灵活的提示释放llm解决复杂问题的潜力。 + +## 4 总结 + +XOT提示技术代表了在激发大型语言模型的能力方面的重大进步。通过将MCTS和LLM知识协同结合,XOT与之前的提示范例相比具有更好的性能、效率和灵活性。XOT产生的灵活的思维结构能够创造性地解决问题,而协作修订过程以最少的LLM交互产生高质量的解决方案。 \ No newline at end of file