Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs
<|reference_start|>Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs: Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. The algorithm equips a LLM with actions for interfacing a KG and enables the LLM to perform tree search over possible thoughts and actions to find high confidence reasoning paths. We evaluate on two popular benchmark datasets. Our results show that Tree-of-Traversals significantly improves performance on question answering and KG question answering tasks. Code is available at \url{https://github.com/amazon-science/tree-of-traversals}<|reference_end|>
@article{markowitz2024tree-of-traversals:,
    title={Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting
  Black-box Language Models with Knowledge Graphs},
    author={Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi,
  Charith Peris, Rahul Gupta, Kai-Wei Chang and Aram Galstyan},
    journal={arXiv preprint arXiv:2407.21358},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21358},
    primaryClass={cs.AI}
}