SubmissionNumber#=%=#51
FinalPaperTitle#=%=#GAVx at SemEval-2024 Task 10: Emotion Flip Reasoning via Stacked Instruction Finetuning of LLMs
ShortPaperTitle#=%=#
NumberOfPages#=%=#11
CopyrightSigned#=%=#Vy Nguyen
JobTitle#==#
Organization#==#RMIT University
Abstract#==#The Emotion Flip Reasoning task at SemEval 2024 aims at identifying the utterance(s) that trigger a speaker to shift from an emotion to another in a multi-party conversation. The spontaneous, informal, and occasionally multilingual dynamics of conversations make the task challenging. In this paper, we propose a supervised stacked instruction-based framework to finetune large language models to tackle this task. Utilising the annotated datasets provided, we curate multiple instruction sets involving chain-of-thoughts, feedback, and self-evaluation instructions, for a multi-step finetuning pipeline. We utilise the self-consistency inference strategy to enhance prediction consistency. Experimental results reveal commendable performance, achieving mean F1 scores of 0.77 and 0.76 for triggers in the Hindi-English and English-only tracks respectively. This led to us earning the second highest ranking in both tracks.
Author{1}{Firstname}#=%=#Vy Hieu Trieu
Author{1}{Lastname}#=%=#Nguyen
Author{1}{Username}#=%=#vynguyen0205
Author{1}{Email}#=%=#s3964786@rmit.edu.vn
Author{1}{Affiliation}#=%=#RMIT University
Author{2}{Firstname}#=%=#Xiuzhen
Author{2}{Lastname}#=%=#Zhang
Author{2}{Email}#=%=#xiuzhen.zhang@rmit.edu.au
Author{2}{Affiliation}#=%=#RMIT University

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