ROMCIR 2026 综述：第六届通过可信信息检索减少在线错误信息研讨会
Overview of ROMCIR 2026: The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval
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中文总结 AI 辅助
本研讨会综述聚焦于通过可信信息检索减少在线错误信息，旨在整合可信度因素、实现早期检测，并评估大语言模型与人在回路的作用。
中文摘要 AI 辅助
在数字在线生态系统中，我们被各种形式的信息污染所包围，对个人和社会都构成重大威胁。例如，假新闻有能力左右公众在政治和金融事务上的舆论。欺骗性评论可能提升或损害企业的声誉，而未经验证的医疗建议可能引导人们走向有害的健康行为。鉴于这一充满挑战的形势，确保用户能够获取既具有主题相关性又事实准确、且不会扭曲其对现实认知的信息变得至关重要，并且通过不同情境和多项任务来打击错误信息的各种策略引起了广泛关注。多年来，ROMCIR 研讨会的宗旨正是吸引信息检索社区探索超越传统错误信息检测方法的潜在解决方案。关键目标包括：识别与信息可信度和真实性分别相关的主观和客观因素，并将这些因素作为信息检索系统（IRSs）中相关性的基本维度加以整合；实现错误信息的早期检测；确保检索到的搜索结果不仅真实，而且对IRSs用户具有可解释性。此外，评估生成模型（如大型语言模型（LLMs））在无意中放大错误信息问题方面的作用，以及它们如何被用于支持IRSs，并评估人在回路范式在此背景下的贡献，也至关重要。
英文摘要
In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance, wields power to sway public opinion on matters of politics and finance. Deceptive reviews can either bolster or tarnish the reputation of businesses, while unverified medical advice may steer people toward harmful health practices. In light of this challenging landscape, it has become imperative to ensure that users have access to both topically relevant and factually accurate information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat misinformation through different contexts and multiple tasks. The purpose of the ROMCIR Workshop, for some years now, is precisely that of engaging the Information Retrieval community to explore potential solutions that extend beyond conventional misinformation detection approaches. Key objectives include identifying subjective and objective factors associated with information credibility and truthfulness, respectively, and integrating such factors as fundamental dimensions of relevance within IR Systems (IRSs), achieving early detection of misinformation, and ensuring that the search results retrieved are not only truthful but also explainable to the users of IRSs. Moreover, it is essential to evaluate the role of generative models such as Large Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs, together with the contribution that the human-in-the-loop paradigm can have in this context.
