Evaluating Masked and Generative Language Models on Natural Language Inference for Clinical Trial Data

Core Concepts

Comparing the performance of masked language models and generative language models on a natural language inference task for clinical trial data, focusing on metrics of faithfulness and consistency.

Abstract

The paper describes two approaches to address the SemEval 2024 Task 2 on Safe Biomedical Natural Language Inference for Clinical Trials (NLI4CT):

1. Finetuning and ensembling Masked Language Models (MLMs) such as NLI-RoBERTa, ClinicalBERT, and Clinical-Longformer. The ensemble of these MLMs achieved 0.57 F1 score, 0.64 Faithfulness, and 0.56 Consistency.

2. Prompting Large Language Models (LLMs) like Flan-T5-large using techniques like Chain-of-Thought and Contrastive Chain-of-Thought in zero-shot, 1-shot, and 2-shot settings. The best LLM system using 2-shot prompting achieved the same performance as the MLM ensemble.

The authors analyze the results in depth, breaking down the performance by gold labels, comparison of clinical trial reports, types of inference, and clinical trial report sections. They find that the MLM approach is more computationally efficient compared to the LLM approach, while achieving similar performance.

The authors also discuss potential future work, including continued pretraining of MLMs on clinical trial data and incorporating domain ontologies to improve the models' performance.

Stats

The NLI4CT dataset consists of 999 clinical trial reports and 2,400 statements.
The average length of a statement is 19.5 tokens, and the average length of an evidence is 10.7 tokens.
The maximum length of a statement is 65 tokens, and the maximum length of an evidence is 197 tokens.
The dataset is balanced, with 50% of instances labeled as Entailment and 50% as Contradiction.

Quotes

"Prompting Flan-T5-large in a 2-shot setting leads to our best system that achieves 0.57 F1 score, 0.64 Faithfulness, and 0.56 Consistency."
"The ensemble of 3 NLI-RoBERTa does not add enough diversity to improve its results."
"The single ClinicalBERT obtains an F1-score of 0.00: we observed that it always predicts the label Contradiction, which causes a precision and recall of 0.00."

Key Insights Distilled From

SEME at SemEval-2024 Task 2

by Mathilde Agu... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03977.pdf

Deeper Inquiries

What other types of language models, such as domain-specific or multi-modal models, could be explored to further improve the performance on this task

To further improve performance on the task of Natural Language Inference for Clinical Trials, exploring domain-specific language models tailored to the medical domain could be beneficial. Models like MEDITRON, which are specifically designed for medical text, could provide more accurate and contextually relevant predictions. Additionally, incorporating multi-modal models that can process both text and images from clinical trials could offer a more comprehensive understanding of the data, leading to improved inference outcomes. Models like CLIP, which can understand text and images together, could be a valuable addition to the system.

How could the prompting techniques be extended or combined with other approaches, such as few-shot learning or data augmentation, to enhance the consistency and faithfulness of the models

Extending the prompting techniques used in the study with few-shot learning approaches could enhance the consistency and faithfulness of the models. By incorporating few-shot learning, the models can adapt quickly to new tasks or data with minimal examples, improving their performance on unseen data. Additionally, combining prompting techniques with data augmentation methods such as back-translation or synonym replacement could help in generating more diverse and robust prompts, leading to better model generalization and performance. This combination could provide a more comprehensive and effective approach to natural language inference tasks.

Given the potential impact of these models on clinical decision-making, what additional ethical considerations or safeguards should be put in place to ensure the responsible development and deployment of such systems

Given the potential impact of these models on clinical decision-making, it is crucial to address additional ethical considerations and safeguards to ensure responsible development and deployment. One key consideration is the transparency and interpretability of the models, ensuring that clinicians can understand how the models arrive at their predictions. Implementing explainable AI techniques can help in providing insights into the model's decision-making process, increasing trust and accountability.
Furthermore, robust data privacy and security measures should be in place to protect sensitive patient information contained in clinical trials. Adhering to strict data governance protocols, such as anonymization and encryption, can mitigate the risks of data breaches or misuse. Additionally, continuous monitoring and auditing of the models' performance and biases are essential to detect and address any ethical issues that may arise during deployment. Collaborating with medical professionals and ethicists in the development process can provide valuable perspectives on the ethical implications of using these models in clinical settings.