This paper presents a novel data augmentation technique to enhance the robustness of natural language inference (NLI) models for analyzing biomedical content, particularly clinical trial reports (CTRs). The key highlights are:
Numerical Question-Answering Task Generation: The authors leverage GPT-3.5 to generate synthetic data for a numerical question-answering task, aiming to improve the model's capabilities in numerical and quantitative reasoning.
Semantic Perturbation: The authors use GPT-3.5 to generate both semantically-preserving and semantically-altering variants of the original entailed statements, expanding the diversity of the training data.
Vocabulary Replacement: The authors employ biomedical knowledge graphs and statistical methods to identify and replace domain-specific keywords in the original statements, further enhancing the model's understanding of the biomedical vocabulary.
Multi-Task Learning: The authors combine the main NLI task with the numerical question-answering task, leveraging the complementary strengths of these objectives to improve the model's overall performance and robustness.
Evaluation: The authors evaluate their approach on the NLI4CT 2024 dataset, demonstrating significant improvements in faithfulness and consistency compared to the original DeBERTa models. Their best-performing model ranked 12th in terms of faithfulness and 8th in terms of consistency out of 32 participants.
The authors discuss the trade-offs between improving robustness to interventions and maintaining strong performance on the original data, and propose future directions to address these challenges.
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by Yuqi Wang,Ze... at arxiv.org 04-16-2024
https://arxiv.org/pdf/2404.09206.pdfDeeper Inquiries