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:
10.18653/v1/2024.semeval-1.222
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.semeval-1.222.pdf
Supplementary material:
 2024.semeval-1.222.SupplementaryMaterial.txt