Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
<|reference_start|>Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification: Within few-shot learning, in-context learning (ICL) has become a potential method for leveraging contextual information to improve model performance on small amounts of data or in resource-constrained environments where training 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 employ the chi-square test to identify high-quality samples and compare the results with those obtained using low-quality samples. Our findings demonstrate that utilizing high-quality samples leads to improved performance with respect to all evaluated metrics.<|reference_end|>
@article{rasheed2024mashee,
    title={Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the
  Performance of In-Context Learning for Machine Text Classification},
    author={Areeg Fahad Rasheed, M. Zarkoosh},
    journal={arXiv preprint arXiv:2406.17790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17790},
    primaryClass={cs.CL}
}