{"type":"ScholarlyArticle","title":"KG-CoT: Chain-of-Thought Prompting of Large Language Models over Knowledge Graphs for Knowledge-Aware Question Answering","authors":[{"name":"Ruilin Zhao","slug":"ruilin-zhao","given":"Ruilin","family":"Zhao","affiliation":"Natural Language Processing and Knowledge Graph Lab , School of Computer Science and Technology , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Feng Zhao","slug":"feng-zhao","given":"Feng","family":"Zhao","affiliation":"Natural Language Processing and Knowledge Graph Lab , School of Computer Science and Technology , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Long Wang","slug":"long-wang","given":"Long","family":"Wang","affiliation":"Natural Language Processing and Knowledge Graph Lab , School of Computer Science and Technology , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Xianzhi Wang","slug":"xianzhi-wang","given":"Xianzhi","family":"Wang","affiliation":"Data Science and Machine Intelligence Lab , University of Technology Sydney , Sydney , Australia"},{"name":"Guandong Xu","slug":"guandong-xu","given":"Guandong","family":"Xu","affiliation":"Data Science and Machine Intelligence Lab , University of Technology Sydney , Sydney , Australia; The Education University of Hong Kong , Hong Kong , China"}],"abstract":"Large language models (LLMs) encounter challenges such as hallucination and factual errors in knowledge-intensive tasks. One the one hand, LLMs sometimes struggle to generate reliable answers based on the black-box parametric knowledge, due to the lack of responsible knowledge. Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. Extensive experiments demonstrate that our KG-CoT significantly improves the performance of LLMs on knowledge-intensive question answering tasks, such as  multi-hop, single-hop, and open-domain question answering benchmarks, without fine-tuning LLMs. KG-CoT outperforms the CoT prompting as well as prior retrieval-augmented and knowledge base question answering baselines. Moreover, KG-CoT can reduce the number of API calls and cost and generalize to various LLM backbones in a lightweight plug-and-play manner.","doi":"10.24963/ijcai.2024/734","url":"https://ijcai.org/proceedings/2024/734","pdf":"https://ijcai.org/proceedings/2024/0734.pdf","markdown":"https://ijcai.org/proceedings/2024/734.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/734","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":734,"pages":{"first":6642,"last":6650},"keywords":["Natural Language Processing: NLP: Language generation","Natural Language Processing: NLP: Question answering"],"videos":[],"references":[{"position":1,"raw":"[Baek et al., 2023] Jinheon Baek, Alham Fikri Aji, Jens Lehmann, and Sung Ju Hwang. 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