Lisbon Computational Linguists At Semeval-2024 Task 2: Using A Mistral 7B Model And Data Augmentation

Guimarães Artur, Martins Bruno, Magalhães João. Proceedings of the 2024

[Paper]    
Prompting Training Techniques

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

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