SubmissionNumber#=%=#37
FinalPaperTitle#=%=#yangqi at SemEval-2024 Task 9: Simulate Human Thinking by Large Language Model for Lateral Thinking Challenges
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Qi Yang
JobTitle#==#
Organization#==#Dalian University of Technology, China
Abstract#==#This paper describes our system used in the SemEval-2024 Task 9 on two sub-tasks, BRAINTEASER: A Novel Task Defying Common Sense. In this work, we developed a system SHTL, which means simulate human thinking capabilities by Large Language Model (LLM). Our approach bifurcates into two main components: Common Sense Reasoning and Rationalize Defying Common Sense. To mitigate the hallucinations of LLM, we implemented a strategy that combines Retrieval-augmented Generation (RAG) with the the Self-Adaptive In-Context Learning (SAICL), thereby sufficiently leveraging the powerful language ability of LLM. The effectiveness of our method has been validated by its performance on the test set, with an average performance on two subtasks that is 30.1 higher than ChatGPT setting zero-shot and only 0.8 lower than that of humans.
Author{1}{Firstname}#=%=#Qi
Author{1}{Lastname}#=%=#Yang
Author{1}{Username}#=%=#yangqi
Author{1}{Email}#=%=#2665643739@mail.dlut.edu.cn
Author{1}{Affiliation}#=%=#Dalian University of Technology
Author{2}{Firstname}#=%=#Jingjie
Author{2}{Lastname}#=%=#Zeng
Author{2}{Email}#=%=#jjtail@mail.dlut.edu.cn
Author{2}{Affiliation}#=%=#Dalian University of Technology
Author{3}{Firstname}#=%=#Liang
Author{3}{Lastname}#=%=#Yang
Author{3}{Email}#=%=#liang@dlut.edu.cn
Author{3}{Affiliation}#=%=#Dalian University of Technology
Author{4}{Firstname}#=%=#Hongfei
Author{4}{Lastname}#=%=#Lin
Author{4}{Email}#=%=#hflin@mail.dlut.edu.cn
Author{4}{Affiliation}#=%=#Dalian University of Technology

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