基于新颖性的树状思维搜索用于大语言模型推理与规划
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
AI总结：
本文提出基于新颖性的树状思维搜索方法，通过引入新颖性概念优化语言领域推理与规划任务，减少搜索范围和token成本。
摘要：
尽管链式思维、树状思维或强化学习等进展提升了大语言模型在推理和规划任务中的性能，但它们仍然脆弱且在许多领域尚未达到人类水平，且常面临高时间和token成本。受宽度搜索在规划中的成功启发，本文探讨如何将新颖性概念转移至语言领域，并如何提升树状思维推理。树状思维依赖于构建连续想法或思维的可能路径，这些路径通过反复提示大语言模型生成。本文提出一个可衡量的新颖性概念，描述新节点（思维）与搜索树中先前节点的唯一性。新颖性通过提示大语言模型并利用预训练的嵌入通用知识进行估计。此指标可用于修剪分支并减少搜索范围。虽然此方法每个状态引入更多提示，但通过修剪和减少整体树大小可降低总体token成本。此过程通过语言领域规划和一般推理的多个基准测试和比较进行测试。
英文摘要：
Although advances such as chain-of-thought, tree-of-thought or reinforcement learning have improved the performance of LLMs in reasoning and planning tasks, they are still brittle and have not achieved human-level performance in many domains, and often suffer from high time and token costs. Inspired by the success of width-based search in planning, we explore how the concept of novelty can be transferred to language domains and how it can improve tree-of-thought reasoning. A tree of thoughts relies on building possible "paths" of consecutive ideas or thoughts. These are generated by repeatedly prompting an LLM. In our paper, a measurable concept of novelty is proposed that describes the uniqueness of a new node (thought) in comparison to nodes previously seen in the search tree. Novelty is estimated by prompting an LLM and making use of embedded general knowledge from pre-training. This metric can then be used to prune branches and reduce the scope of the search. Although this method introduces more prompts per state, the overall token cost can be reduced by pruning and reducing the overall tree size. This procedure is tested and compared using several benchmarks in language-based planning and general reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
