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.- Anthology ID:
- 2024.semeval-1.10
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 60–63
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.10
- DOI:
- Cite (ACL):
- Areeg Fahad Rasheed and M. Zarkoosh. 2024. Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 60–63, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification (Rasheed & Zarkoosh, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.10.pdf