SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
Elisei Rykov, Yana Shishkina, Ksenia Petrushina, Ksenia Titova, Sergey Petrakov, Alexander Panchenko
Abstract
In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards, encompassing diverse baselines, the refinement of pre-trained encoders through supervised learning, and an ensemble approaches utilizing several high-performing models. Through these explorations, we introduce three distinct methods that exhibit strong performance metrics. To amplify our training data, we generate additional training samples from unlabelled training subset. Furthermore, we provide a detailed comparative analysis of our approaches. Notably, our premier method achieved a commendable 9th place in the competition’s model-agnostic track and 20th place in model-aware track, highlighting its effectiveness and potential.- Anthology ID:
- 2024.semeval-1.125
- 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:
- 869–880
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.125
- DOI:
- Cite (ACL):
- Elisei Rykov, Yana Shishkina, Ksenia Petrushina, Ksenia Titova, Sergey Petrakov, and Alexander Panchenko. 2024. SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 869–880, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection (Rykov et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.125.pdf