ClimateCheck 2026：气候相关主张的科学事实核查与虚假信息叙述分类
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims
AI总结：
ClimateCheck 2026通过扩大训练数据和新增虚假信息叙述分类任务，挑战气候相关主张的自动核查，揭示传统指标的系统性偏差。
摘要：
自动验证气候相关主张 against 科学文献是一项具有挑战性的任务，复杂性源于学术证据的专业性和气候虚假信息背后的多样化修辞策略。ClimateCheck 2026是针对这一挑战的共享任务第二版，扩展了2025版，训练数据三倍增加并新增了虚假信息叙述分类任务。该竞赛于2026年1月至2月在CodaBench平台运行，吸引了20名注册参与者和8份排行榜提交，系统结合密集检索流水线、交叉编码器集合和具有结构化层级推理的大语言模型。除了标准评估指标（Recall@K和二元偏好），我们还适应了自动化框架来评估在不完整标注下的检索质量，揭示了传统指标如何对系统进行排名的系统性偏差。跨任务分析进一步揭示并非所有气候虚假信息都同样可验证，这可能影响未来事实核查系统的设计。
英文摘要：
Automatically verifying climate-related claims against scientific literature is a challenging task, complicated by the specialised nature of scholarly evidence and the diversity of rhetorical strategies underlying climate disinformation. ClimateCheck 2026 is the second iteration of a shared task addressing this challenge, expanding on the 2025 edition with tripled training data and a new disinformation narrative classification task. Running from January to February 2026 on the CodaBench platform, the competition attracted 20 registered participants and 8 leaderboard submissions, with systems combining dense retrieval pipelines, cross-encoder ensembles, and large language models with structured hierarchical reasoning. In addition to standard evaluation metrics (Recall@K and Binary Preference), we adapt an automated framework to assess retrieval quality under incomplete annotations, exposing systematic biases in how conventional metrics rank systems. A cross-task analysis further reveals that not all climate disinformation is equally verifiable, potentially implicating how future fact-checking systems should be designed.
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。
