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A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration

Authors: Zhang Zheng

Published: 2024-09-16

arXiv ID: 2409.10403v1

Added to Library: 2025-11-11 14:02 UTC

📄 Abstract

This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning capabilities for the task.We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the critical role of the knowledge injection module,as the removal of this module resulted in a significant drop in F1 score. The experimental results demonstrate that the proposed method not only effectively improves the accuracy of disease diagnosis but also enhances the interpretability of the predictions, providing more reliable support and evidence for clinical diagnosis.

🔍 Key Points

  • Introduction of DataSentinel, a game-theoretic method that enhances prompt injection attack detection using fine-tuned LLMs.
  • Formulation of detection as a minimax optimization problem considering both detection LLM fine-tuning and adaptive attacks.
  • Demonstrated effectiveness of DataSentinel through evaluations on diverse benchmark datasets and multiple LLMs, achieving near-zero false positive and negative rates.
  • Showcased significant performance improvements over existing baseline methods, particularly for adaptive prompt injection attacks, indicating practical application for real-world LLM integrations.

💡 Why This Paper Matters

This paper introduces a novel approach to detecting prompt injection attacks using game-theoretic principles. By fine-tuning LLMs to discern clean from contaminated data, the authors provide a robust defense mechanism that adapts to evolving attack strategies. The effectiveness of DataSentinel across various tasks highlights its potential impact on enhancing the security of LLM-integrated applications.

🎯 Why It's Interesting for AI Security Researchers

The research is crucial for AI security researchers focused on ensuring the integrity and reliability of LLM applications. As prompt injection attacks become more sophisticated, understanding and mitigating these vulnerabilities are paramount for developing secure AI systems. The paper's innovative methodology and promising results present valuable insights and tools for the AI security community.

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