AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
Natalia Grigoriadou, Maria Lymperaiou, George Filandrianos, Giorgos Stamou
Abstract
In this paper, we present our team’s submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants were asked to perform binary classification to identify cases of fluent overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model on hallucination detection and a Natural Language Inference (NLI) model. The most successful strategy involved creating an ensemble of these models, resulting in accuracy rates of 77.8% and 79.9% on model-agnostic and model-aware datasets respectively, outperforming the organizers’ baseline and achieving notable results when contrasted with the top-performing results in the competition, which reported accuracies of 84.7% and 81.3% correspondingly.- Anthology ID:
- 2024.semeval-1.222
- 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:
- 1549–1560
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.222
- DOI:
- Cite (ACL):
- Natalia Grigoriadou, Maria Lymperaiou, George Filandrianos, and Giorgos Stamou. 2024. AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1549–1560, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis (Grigoriadou et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.222.pdf