通过多模态声明检测与基于证据的验证对抗生物医学错误信息
Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification
AI总结：
针对生物医学声明验证的挑战，提出了CER框架，结合科学证据检索、大语言模型推理与有监督真实性预测，有效缓解幻觉并实现了最先进的跨数据集泛化性能。
摘要：
医疗保健领域的错误信息，从疫苗犹豫到未经证实的治疗方法，对公共健康和医疗系统的信任构成了风险。尽管机器学习和自然语言处理推动了自动化事实核查的发展，但由于术语复杂、需要领域专业知识，以及基于科学证据的至关重要性，验证生物医学声明仍面临独特挑战。我们提出了CER（Combining Evidence and Reasoning，结合证据与推理），这是一个用于生物医学事实核查的新颖框架，它集成了科学证据检索、通过大型语言模型进行的推理以及有监督的真实性预测。通过将大型语言模型的文本生成能力与高质量生物医学科学证据的先进检索技术相结合，CER有效缓解了幻觉风险，确保生成的输出基于可验证的、循证的来源。在专家标注的数据集（HealthFC、BioASQ-7b、SciFact）上的评估表明，该框架达到了最先进的性能，并展现出良好的跨数据集泛化能力。代码和数据已开源以确保透明度和可复现性：https://github.com/PRAISELab-PicusLab/CER
英文摘要：
Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
