Byun at SemEval-2024 Task 6: Text Classification on Hallucinating Text with Simple Data Augmentation
Abstract
This paper aims to classify sentences to see if it is hallucinating, meaning the generative language model has output text that has very little to do with the user’s input, or not. This classification task is part of the Semeval 2024’s task on Hallucinations and Related Observable Over-generation Mistakes, AKA SHROOM, which aims to improve awkward-sounding texts generated by AI. This paper will first go over the first attempt at creating predictions, then show the actual scores achieved after submitting the first attempt results to Semeval, then finally go over potential improvements to be made.- Anthology ID:
- 2024.semeval-1.41
- 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:
- 270–273
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.41
- DOI:
- Cite (ACL):
- Cheolyeon Byun. 2024. Byun at SemEval-2024 Task 6: Text Classification on Hallucinating Text with Simple Data Augmentation. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 270–273, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Byun at SemEval-2024 Task 6: Text Classification on Hallucinating Text with Simple Data Augmentation (Byun, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2024.semeval-1.41.pdf