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While large language models (LLMs) have shown remarkable capabilities in natural language processing, they struggle with complex, multi-step reasoning tasks involving knowledge graphs (KGs).
Language models are few-shot learners
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Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph
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Challenging big-bench tasks and whether chain-of-thought can solve them
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Self-consistency improves chain of thought reasoning in language models
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Chain-of-thought prompting elicits reasoning in large language models
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Decaf: Joint decoding of answers and logical forms for question answering over knowledge bases
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Knowledgenavigator: Leveraging large language models for enhanced reasoning over knowledge graph
Tiezheng Guo, Qingwen Yang, Chen Wang, Yanyi Liu, Pan Li, Jiawei Tang, Dapeng Li, and Yingyou Wen. 2023 · 2023
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Graph of thoughts: Solving elaborate problems with large language models
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