Lisbon Computational Linguists at SemEval-2024 Task 2: Using a Mistral-7B Model and Data Augmentation

Artur Guimarães, Bruno Martins, João Magalhães


Abstract
ABSTRACT: This paper describes our approach to the SemEval-2024 safe biomedical Natural Language Inference for Clinical Trials (NLI4CT) task, which concerns classifying statements about Clinical Trial Reports (CTRs). We explored the capabilities of Mistral-7B, a generalistic open-source Large Language Model (LLM). We developed a prompt for the NLI4CT task, and fine-tuning a quantized version of the model using a slightly augmented version of the training dataset. The experimental results show that this approach can produce notable results in terms of the macro F1-score, while having limitations in terms of faithfulness and consistency. All the developed code is publicly available on a GitHub repository.
Anthology ID:
2024.semeval-1.185
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:
1280–1287
Language:
URL:
https://aclanthology.org/2024.semeval-1.185
DOI:
10.18653/v1/2024.semeval-1.185
Bibkey:
Cite (ACL):
Artur Guimarães, Bruno Martins, and João Magalhães. 2024. Lisbon Computational Linguists at SemEval-2024 Task 2: Using a Mistral-7B Model and Data Augmentation. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1280–1287, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Lisbon Computational Linguists at SemEval-2024 Task 2: Using a Mistral-7B Model and Data Augmentation (Guimarães et al., SemEval 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.185.pdf
Supplementary material:
 2024.semeval-1.185.SupplementaryMaterial.txt