@inproceedings{rasheed-zarkoosh-2024-mashee,
title = "Mashee at {S}em{E}val-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification",
author = "Rasheed, Areeg Fahad and
Zarkoosh, M.",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.10",
doi = "10.18653/v1/2024.semeval-1.10",
pages = "60--63",
abstract = "Within few-shot learning, in-context learning(ICL) has become a potential method for lever-aging contextual information to improve modelperformance on small amounts of data or inresource-constrained environments where train-ing models on large datasets is prohibitive.However, the quality of the selected samplein a few shots severely limits the usefulnessof ICL. The primary goal of this paper is toenhance the performance of evaluation metricsfor in-context learning by selecting high-qualitysamples in few-shot learning scenarios. We em-ploy the chi-square test to identify high-qualitysamples and compare the results with those ob-tained using low-quality samples. Our findingsdemonstrate that utilizing high-quality samplesleads to improved performance with respect toall evaluated metrics.",
}
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%0 Conference Proceedings
%T Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
%A Rasheed, Areeg Fahad
%A Zarkoosh, M.
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F rasheed-zarkoosh-2024-mashee
%X Within few-shot learning, in-context learning(ICL) has become a potential method for lever-aging contextual information to improve modelperformance on small amounts of data or inresource-constrained environments where train-ing models on large datasets is prohibitive.However, the quality of the selected samplein a few shots severely limits the usefulnessof ICL. The primary goal of this paper is toenhance the performance of evaluation metricsfor in-context learning by selecting high-qualitysamples in few-shot learning scenarios. We em-ploy the chi-square test to identify high-qualitysamples and compare the results with those ob-tained using low-quality samples. Our findingsdemonstrate that utilizing high-quality samplesleads to improved performance with respect toall evaluated metrics.
%R 10.18653/v1/2024.semeval-1.10
%U https://aclanthology.org/2024.semeval-1.10
%U https://doi.org/10.18653/v1/2024.semeval-1.10
%P 60-63
Markdown (Informal)
[Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification](https://aclanthology.org/2024.semeval-1.10) (Rasheed & Zarkoosh, SemEval 2024)
ACL