Summary of Tree-of-traversals: a Zero-shot Reasoning Algorithm For Augmenting Black-box Language Models with Knowledge Graphs, by Elan Markowitz et al.
Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs
by Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables the augmentation of black-box Large Language Models (LLMs) with one or more knowledge graphs (KGs). The algorithm equips LLMs with actions for interfacing KGs and allows them to perform tree search over possible thoughts and actions to find high-confidence reasoning paths. This integration improves performance on question answering and knowledge graph-based question answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tree-of-Traversals is a new way to combine Large Language Models (LLMs) with Knowledge Graphs (KGs). It helps LLMs understand KGs better and make good decisions. The researchers tested it on two big datasets and found that it works really well. This means we can use LLMs in more ways, like answering tricky questions. |
Keywords
* Artificial intelligence * Knowledge graph * Question answering * Zero shot
