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English(EN) Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs

Framework of Thoughts 通过动态优化增强 LLM 推理能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出 Framework of Thoughts (FoT)，这是一个新的基础框架，旨在增强大型语言模型 (LLM) 的动态和优化推理能力。现有的思维链 (Chain of Thought)、思维树 (Tree of Thoughts) 和思维图 (Graph of Thoughts) 等提示方案通常需要静态的、针对特定问题的结构，并且可能效率低下。FoT 旨在通过整合超参数调整、提示优化、并行执行和智能缓存等功能来解决这些问题。该框架通过实现和优化 Tree of Thoughts、Graph of Thoughts 和 ProbTree 等流行方案得到了验证，在执行速度、成本降低和任务性能方面均有显著提升。 AI

影响 该框架有望实现更高效、更具适应性的 LLM 推理，从而提高复杂任务的性能。

排序理由 这是一篇描述 LLM 推理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Framework of Thoughts 通过动态优化增强 LLM 推理能力

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这是一篇描述 LLM 推理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Felix Fricke, Simon Malberg, Georg Groh ·

    Framework of Thoughts：一种基于链、树和图的动态优化推理基础框架

    arXiv:2602.16512v2 Announce Type: replace Abstract: Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, pr…

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