Elan Markowitz
2024
Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs
Elan Markowitz
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Anil Ramakrishna
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Jwala Dhamala
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Ninareh Mehrabi
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Charith Peris
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Rahul Gupta
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Kai-Wei Chang
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Aram Galstyan
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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. Tree-of-Traversals significantly improves performance on question answering and KG question answering tasks. Code is available at https://github.com/amazon-science/tree-of-traversals
2022
StATIK: Structure and Text for Inductive Knowledge Graph Completion
Elan Markowitz
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Keshav Balasubramanian
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Mehrnoosh Mirtaheri
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Murali Annavaram
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Aram Galstyan
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Greg Ver Steeg
Findings of the Association for Computational Linguistics: NAACL 2022
Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing links. In this paper, we present StATIK–Structure And Text for Inductive Knowledge Completion. StATIK uses Language Models to extract the semantic information from text descriptions, while using Message Passing Neural Networks to capture the structural information. StATIK achieves state of the art results on three challenging inductive baselines. We further analyze our hybrid model through detailed ablation studies.
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Co-authors
- Aram Galstyan 2
- Keshav Balasubramanian 1
- Mehrnoosh Mirtaheri 1
- Murali Annavaram 1
- Greg Ver Steeg 1
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