SubmissionNumber#=%=#119
FinalPaperTitle#=%=#YNU-HPCC at SemEval-2024 Task 1: Self-Instruction Learning with Black-box Optimization for Semantic Textual Relatedness
ShortPaperTitle#=%=#
NumberOfPages#=%=#8
CopyrightSigned#=%=#WeiJie Li
JobTitle#==#
Organization#==#
Abstract#==#This paper introduces a system designed for SemEval-2024 Task 1 that focuses on assessing Semantic Textual Relatedness (STR) between sentence pairs, including its multilingual version. STR, which evaluates the coherence of sentences, is distinct from Semantic Textual Similarity (STS). However, Large Language Models (LLMs) such as ERNIE-Bot-turbo, typically trained on STS data, often struggle to differentiate between the two concepts. To address this, we developed a self-instruction method that enhances their performance distinguishing STR, particularly in cases with high STS but low STR. Beginning with a task description, the system generates new task instructions refined through human feedback. It then iteratively enhances these instructions by comparing them to the original and evaluating the differences. Utilizing the Large Language Models' (LLMs) natural language comprehension abilities, the system aims to produce progressively optimized instructions based on the resulting scores. Through our optimized instructions, ERNIE-Bot-turbo exceeds the performance of conventional models,achieving a score enhancement of 4 to 7\% on multilingual development datasets.
Author{1}{Firstname}#=%=#Weijie
Author{1}{Lastname}#=%=#Li
Author{1}{Username}#=%=#awei5880
Author{1}{Email}#=%=#13169315880@163.com
Author{1}{Affiliation}#=%=#Yunnan University
Author{2}{Firstname}#=%=#Jin
Author{2}{Lastname}#=%=#Wang
Author{2}{Username}#=%=#wangjin0818
Author{2}{Email}#=%=#wangjin@ynu.edu.cn
Author{2}{Affiliation}#=%=#Yunnan University
Author{3}{Firstname}#=%=#Xuejie
Author{3}{Lastname}#=%=#Zhang
Author{3}{Username}#=%=#xjzhang
Author{3}{Email}#=%=#xjzhang@ynu.edu.cn
Author{3}{Affiliation}#=%=#Yunnan University

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