用新颖性筛选思路，让大模型推理更高效。
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
- 基于思想新颖性动态剪枝搜索树，避免无效分支。
- 在多个推理任务中减少约40%的总调用次数。
- 适合需要高质量规划的大模型应用者。
尽管链式思维、思维树或强化学习等方法提升了大模型在推理与规划任务中的表现，但其仍存在脆弱性，未能在多数领域达到人类水平，且常伴随高时间与令牌开销。受广度优先搜索在规划中成功启发，本文探索将新颖性概念迁移到语言领域，并用于改进思维树推理。思维树通过反复提示大模型生成连续的思想路径。本文提出一种可量化的新颖性度量，用于评估新节点（思想）相较于已有搜索树节点的独特性，该度量通过提示大模型并利用预训练中的通用知识获得。该指标可用于剪枝分支，缩小搜索范围。尽管每状态增加提示次数，但通过剪枝显著减少整体树规模，从而降低总令牌消耗。该方法在多个语言规划与通用推理基准上进行了测试与对比。
原文摘要 · Abstract (English)
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.
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