SubmissionNumber#=%=#10
FinalPaperTitle#=%=#Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
ShortPaperTitle#=%=#
NumberOfPages#=%=#4
CopyrightSigned#=%=#Areeg Fahad Rasheed
JobTitle#==#
Organization#==#
Abstract#==#Within few-shot learning, in-context learning
(ICL) has become a potential method for lever-
aging contextual information to improve model
performance on small amounts of data or in
resource-constrained environments where train-
ing models on large datasets is prohibitive.
However, the quality of the selected sample
in a few shots severely limits the usefulness
of ICL. The primary goal of this paper is to
enhance the performance of evaluation metrics
for in-context learning by selecting high-quality
samples in few-shot learning scenarios. We em-
ploy the chi-square test to identify high-quality
samples and compare the results with those ob-
tained using low-quality samples. Our findings
demonstrate that utilizing high-quality samples
leads to improved performance with respect to
all evaluated metrics.
Author{1}{Firstname}#=%=#Areeg Fahad
Author{1}{Lastname}#=%=#Rasheed
Author{1}{Username}#=%=#areeg94fahad
Author{1}{Email}#=%=#fahedareeg@gmail.com
Author{1}{Affiliation}#=%=#Al Nahrain university
Author{2}{Firstname}#=%=#M.
Author{2}{Lastname}#=%=#Zarkoosh
Author{2}{Email}#=%=#m94zarkoosh@gmail.com
Author{2}{Affiliation}#=%=#Freelancer

==========
èéáğö