从科学文献中自动化发现干预措施：一种渐进式本体提示与双大语言模型框架
Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework
AI总结：
针对科学文献中干预措施人工识别效率低易疏漏的问题，提出融合渐进式本体提示算法与双LLM智能体的自动化框架，在言语-语言病理领域语料上效果优于基线，构建了公开干预知识库。
摘要：
由于出版物数量庞大、术语专业且报告格式不统一，从科学文献中识别有效干预措施极具挑战性，人工梳理不仅费力还容易出现疏漏。为解决这一难题，本文提出一种利用大语言模型（LLM）的新型框架，该框架将渐进式本体提示（progressive ontology prompting，POP）算法与名为LLM-Duo的双智能体系统相结合。一方面，POP算法在预定义本体上执行带优先级的广度优先搜索（BFS），生成结构化提示模板与动作序列，以引导自动标注过程。另一方面，LLM-Duo系统包含两个专门的LLM智能体——探索者与评估者，二者通过协作与对抗的方式持续优化标注质量。我们通过一项聚焦于言语-语言干预发现的案例研究，展示了该框架的实际应用价值。实验结果表明，本文方法优于先进基线模型，通过全自动化流程实现了更准确、更全面的标注。该方法成功从言语-语言病理学领域的64177篇研究论文语料中识别出2421项干预措施，最终构建了一个可公开访问的干预知识库，有望为言语-语言病理学领域带来极大助益。
英文摘要：
Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting formats, making manual curation laborious and prone to oversight. To address this challenge, this paper proposes a novel framework leveraging large language models (LLMs), which integrates a progressive ontology prompting (POP) algorithm with a dual-agent system, named LLM-Duo. On the one hand, the POP algorithm conducts a prioritized breadth-first search (BFS) across a predefined ontology, generating structured prompt templates and action sequences to guide the automatic annotation process. On the other hand, the LLM-Duo system features two specialized LLM agents, an explorer and an evaluator, working collaboratively and adversarially to continuously refine annotation quality. We showcase the real-world applicability of our framework through a case study focused on speech-language intervention discovery. Experimental results show that our approach surpasses advanced baselines, achieving more accurate and comprehensive annotations through a fully automated process. Our approach successfully identified 2,421 interventions from a corpus of 64,177 research articles in the speech-language pathology domain, culminating in the creation of a publicly accessible intervention knowledge base with great potential to benefit the speech-language pathology community.
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。
