,

Catherine Kosten1, Farhad Nooralahzadeh1, Kurt Stockinger1

  • 1School of Engineering, Institute of Computer Science, Intelligent Information Systems Research Group, Zurich University of Applied Sciences, Winterthur, Switzerland.

概括

在复杂的知识图问题解答 (KGQA) 任务上,评估了大型语言模型 (LLM) 的六种几次拍摄提示方法,这项研究发现,一个简单的提示,一个本体学和五次随机拍摄是最有效的. 这种方法为优化LLM业绩,以应对复杂的KGQA挑战提供了洞察力.

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