With recent advancements in large language models, methods like
chain-of-thought prompting to elicit reasoning chains have been shown to
improve results on reasoning tasks. However, tasks that require multiple steps
of reasoning still pose significant challenges to state-of-the-art models.
Drawing inspiration from the beam search algorithm, we propose PathFinder, a
tree-search-based reasoning path generation approach. It enhances diverse
branching and multi-hop reasoning through the integration of dynamic decoding,
enabled by varying sampling methods and parameters. Using constrained
reasoning, PathFinder integrates novel quality constraints, pruning, and
exploration methods to enhance the efficiency and the quality of generation.
Moreover, it includes scoring and ranking features to improve candidate
selection. Our approach outperforms competitive baselines on three complex
arithmetic and commonsense reasoning tasks by 6% on average. Our model
generalizes well to longer, unseen reasoning chains, reflecting similar
complexities to beam search with large branching factors.

引用束搜索算法的灵感，我们提出了 PathFinder，一种基于树搜索的推理路径生成方法，通过集成动态解码、约束推理和评分排序功能，提高多样分支和多跳推理的效率和质量，以及候选选择的性能，相对于竞争性的基线模型，在三个复杂的算术和常识推理任务上表现优于平均值 6%，且在具有大分支因素的束搜索中具有良好的泛化能力。