Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

arXiv:2402.16568v1 Announce Type: new
Abstract: Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over structured data, their application to the TKGQA task is a relatively unexplored area. This paper first proposes a novel generative temporal knowledge graph question answering framework, GenTKGQA, which guides LLMs to answer temporal questions …

abstract arxiv challenge constraints cs.cl data dynamic generative graph hidden knowledge knowledge graph language language models large language large language models llms progress question question answering questions reasoning stage structured data temporal type