Leveraging Data Perturbations and MinMax Training to Develop Robust Large Language Models for Clinical Natural Language Inference

Temel Kavramlar

Developing a large language model-based system that leverages data perturbations and the MinMax training algorithm to enhance robustness in natural language inference on clinical trial reports.

Özet

The authors propose a system that utilizes the state-of-the-art Mistral language model, complemented by an auxiliary model, to address the NLI4CT task at SemEval-2024. The task focuses on developing robust models for Natural Language Inference on Clinical Trial Reports (CTRs) using large language models (LLMs).
The key highlights of the approach are:

Evaluating the zero-shot performance of various instruction-tuned LLMs, with Mistral emerging as the top-performing model.
Incorporating an auxiliary model alongside the Mistral model using the MinMax algorithm to enhance the system's robustness.
Introducing numerical and semantic perturbations to the NLI4CT dataset to improve the model's handling of numerical reasoning and domain-specific terminology.
Conducting a detailed error analysis to identify easy and difficult instances in the training set, providing insights for future research.

The authors' final system ranked 11th in macro F1 score, 12th in Faithfulness, and 19th in Consistency out of 31 participants. The system demonstrated strong performance on semantic-altering interventions but struggled with semantic-preserving interventions on the test data.

İstatistikler

Percentage of Left ventricular systolic dysfunction is higher in cohort 1 than cohort 2.
Outcome Measurement: Event-free Survival, Time frame: 5 years.
Adverse Events 1: LVSD 1/3761 (0.03%)
Adverse Events 2: LVSD 0/3759 (0.00%)

Alıntılar

"The NLI4CT task at SemEval-2024 emphasizes the development of robust models for Natural Language Inference on Clinical Trial Reports (CTRs) using large language models (LLMs)."
"Our proposed system harnesses the capabilities of the state-of-the-art Mistral model (Jiang et al., 2023), complemented by an auxiliary model, to focus on the intricate input space of the NLI4CT dataset."

Önemli Bilgiler Şuradan Elde Edildi

DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training

by Bhuvanesh Ve... : arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00321.pdf

Daha Derin Sorular

How can the authors further improve the system's performance on semantic-preserving interventions in the NLI4CT dataset?

To enhance the system's performance on semantic-preserving interventions in the NLI4CT dataset, the authors can consider several strategies:

Synonym Augmentation: Introducing synonyms into instances with high word overlap can help the model generalize better and reduce reliance on specific word matches.
Fine-tuning on Similar Datasets: Training the model on datasets with similar semantic structures can improve its understanding of nuanced language nuances.
Adversarial Training: Incorporating adversarial training techniques can expose the model to challenging examples, forcing it to learn more robust representations.
Ensemble Methods: Combining predictions from multiple models or checkpoints can help mitigate biases and errors, improving overall performance.
Regularization Techniques: Implementing regularization methods like dropout or weight decay can prevent overfitting and improve generalization on semantic-preserving tasks.

How can the insights gained from the dataset difficulty analysis be leveraged to develop more targeted training strategies for different sections of the clinical trial reports?

The insights from the dataset difficulty analysis can be leveraged to develop targeted training strategies for different sections of clinical trial reports in the following ways:

Section-Specific Fine-tuning: Tailoring fine-tuning processes to focus more on challenging sections like Adverse Events can improve model performance on these specific areas.
Data Augmentation: Generating more instances from difficult sections can help the model learn diverse patterns and improve its understanding of complex information.
Curriculum Learning: Implementing a curriculum learning approach where the model is gradually exposed to more challenging instances can help it build robustness and adaptability.
Feedback Mechanisms: Providing feedback loops based on the model's performance on specific sections can guide training strategies and prioritize areas that need improvement.
Transfer Learning: Leveraging pre-trained models fine-tuned on related datasets or tasks can help the model capture domain-specific nuances and improve performance on specific sections.

What other techniques, beyond data perturbations, could be explored to enhance the model's robustness against numerical reasoning challenges in clinical text?

In addition to data perturbations, several techniques can be explored to enhance the model's robustness against numerical reasoning challenges in clinical text:

Rule-based Systems: Incorporating rule-based systems to handle specific numerical patterns or calculations can provide a structured approach to numerical reasoning tasks.
Domain-specific Embeddings: Utilizing domain-specific embeddings or knowledge graphs can enhance the model's understanding of medical concepts and numerical entities.
Multi-task Learning: Training the model on multiple tasks, including numerical reasoning and language understanding, can improve its ability to handle complex numerical information.
Explainable AI: Implementing explainable AI techniques can help the model justify its numerical reasoning decisions, making the process more transparent and reliable.
Interactive Learning: Introducing interactive learning paradigms where the model can interact with users to clarify numerical information can improve its accuracy and reasoning capabilities in clinical text.