Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data Augmentation
<|reference_start|>Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data Augmentation: 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 generalist open-source Large Language Model (LLM). We developed a prompt for the NLI4CT task, and fine-tuned a quantized version of the model using an 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<|reference_end|>
@article{guimarães2024lisbon,
    title={Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral
  7B Model and Data Augmentation},
    author={Artur Guimar~aes, Bruno Martins and Jo~ao Magalh~aes},
    journal={Proceedings of the 18th International Workshop on Semantic
  Evaluation (SemEval-2024) (2024) 1280-1287},
    year={2024},
    doi={10.18653/v1/2024.semeval-1.185},
    archivePrefix={arXiv},
    eprint={2408.03127},
    primaryClass={cs.CL}
}