ComplexWebQuestions ↔ GPT-4

Relations (1)

related 2.00 — strongly supporting 3 facts

GPT-4 is utilized as a core reasoning model in various knowledge graph question answering frameworks, including ToG [1], KG-CoT [2], and KG-Agent [3], all of which use the ComplexWebQuestions (CWQ) dataset for performance evaluation.

Facts (3)

Sources

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv 3 facts

referenceThe KG-CoT method, proposed by Zhao et al. in 2024, uses chain-of-thought-based joint reasoning between knowledge graphs and LLMs (GPT-4, GPT-3.5-Turbo, Llama-7B, Llama-13B) to perform KBQA and multi-hop QA tasks, evaluated using Acc and Hit@K metrics on WQSP, CWQ, SQ, and WQ datasets.

referenceThe KG-Agent method, proposed by Jiang et al. in 2024, uses KG-Agent-based instruction tuning with Davinci-003, GPT-4, and Llama-2-7B models to perform KGQA and ODQA tasks, evaluated using Hits@1 and F1 metrics on WQSP, CWQ, and GrailQA datasets.

referenceThe ToG method, proposed by Sun et al. in 2024, uses beam-search-based retrieval and LLM agents with GPT-3.5-Turbo, GPT-4, and Llama-2-70B-Chat models to perform KBQA and open-domain QA tasks, evaluated using Hits@1 on CWQ, WQSP, GrailQA, QALD10-en, and WQ datasets.