发表机构
Western Sydney University; Microsoft; Excelsia College; Faulconbridge Health Centre（西悉尼大学; 微软公司; 埃克塞尔西亚学院; 福尔康布里奇健康中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对低资源语言中健康错误信息难检测问题，提出结合小语言模型与文化敏感的负责任自然语言处理框架，以孟加拉语为例进行实验，证明Phi-4表现优，还设计新框架，为评估低资源语言错误信息提供整体视角。
AI 中文摘要
人工智能技术是现代社交媒体和数字健康服务的基础，但同时也是错误信息的传播媒介。在非英语环境和低社会经济阶层中，数据有限阻碍了有效检测的人工智能模型训练。当前的人工智能工具因缺乏训练数据而表现不佳，无法考虑非英语语境中的语言细微差别和传统。本研究提出了一个对文化和语言多样化群体友好的基于人工智能的健康错误信息检测器，并为医疗专业人员提供了一个分析此类错误信息的仪表板。为此，使用孟加拉语翻译的健康错误信息数据集进行了一系列实验，以评估各种小语言模型的性能。结果表明Phi-4是 superior 模型。为减轻小语言模型的局限性，设计并测试了一个基于负责任自然语言处理的新型健康错误信息检测框架，该框架纳入了文化敏感性、潜在危害和沟通质量，为评估低资源语言中的错误信息提供了一个整体视角。
英文摘要
Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation. A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection. Consequently, culturally and linguistically diverse (CALD) communities struggle to access trustworthy health information through AI-driven tools. Current AI tools underperform due to a lack of training data and are largely unable to consider language nuances and traditions in non-English contexts. This research addresses these gaps by proposing a CALD-friendly AI-based health misinformation detector and providing a dashboard for medical professionals to analyse this misinformation, a critical step toward mitigating a growing concern among CALD populations. To this end, we conduct a series of experiments using a Bangla-translated health misinformation dataset to evaluate the performance of various Small Language Models (SLMs). SLMs are particularly relevant in this context given the frequent underperformance of Large Language Models (LLMs), which often stems from insufficient domain-specific knowledge and the prohibitive costs of resource-intensive fine-tuning. The results demonstrate that Phi-4 is the superior model, achieving an ideal balance between precision and recall in claim extraction. Then, to mitigate the limitations of SLMs, we design and test a novel health misinformation detection framework grounded in Responsible Natural Language Processing (NLP), which incorporates cultural sensitivity, potential for harm, and communication quality, thereby providing a holistic lens for evaluating misinformation in low-resource languages.
Comments39 pages
