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related 2.32 — strongly supporting 4 facts
Question Answering is the primary application for LLM knowledge graph integration, as evidenced by the development of benchmarks like ChatData [1], XplainLLM [2], and LLM-KG-Bench [3], as well as the release of the Docugami KG-RAG datasets [4] specifically designed for this purpose.
Facts (4)
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LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com 4 facts
referenceThe paper 'XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs' published in EMNLP in 2024 introduces the XplainLLM dataset for LLM and Knowledge Graph integration in question answering.
referenceThe Docugami Knowledge Graph Retrieval Augmented Generation (KG-RAG) datasets were released in 2023 for LLM and Knowledge Graph integration in question answering.
referenceThe paper 'A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases' published in GRADES-NDA in 2024 introduces the ChatData benchmark for LLM and Knowledge Graph integration in question answering.
referenceThe paper 'Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering' published in SEMANTICS in 2023 introduces the LLM-KG-Bench benchmark for LLM and Knowledge Graph integration in question answering.