Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data Augmentation
This work addresses biomedical natural language inference for clinical trials, but it is incremental as it applies an existing LLM with standard fine-tuning and augmentation techniques.
The authors tackled the SemEval-2024 safe biomedical NLI task by fine-tuning a quantized Mistral 7B model with data augmentation, achieving notable macro F1-score results but with limitations in faithfulness and consistency.
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