We present EvasionBench, a comprehensive benchmark for detecting evasive responses in corporate earnings call question-and-answer sessions. Drawing from 22.7 million Q&A pairs extracted from S&P Capital IQ transcripts, we construct a rigorously filtered dataset and introduce a three-level evasion taxonomy: direct, intermediate, and fully evasive. Our annotation pipeline employs a Multi-Model Consensus (MMC) framework, combining dual frontier LLM annotation with a three-judge majority voting mechanism for ambiguous cases, achieving a Cohen's Kappa of 0.835 on human inter-annotator agreement. We release: (1) a balanced 84K training set, (2) a 1K gold-standard evaluation set with expert human labels, and (3) [Eva-4B], a 4-billion parameter classifier fine-tuned from Qwen3-4B that achieves 84.9% Macro-F1, outperforming Claude 4.5, GPT-5.2, and Gemini 3 Flash. Our ablation studies demonstrate the effectiveness of multi-model consensus labeling over single-model annotation. EvasionBench fills a critical gap in financial NLP by providing the first large-scale benchmark specifically targeting managerial communication evasion.

翻译：我们提出了EvasionBench，这是一个用于检测公司财报电话会议问答环节中规避性回答的综合基准。基于从标普Capital IQ文本记录中提取的2270万个问答对，我们构建了一个经过严格筛选的数据集，并引入了一个三级规避分类法：直接规避、中度规避和完全规避。我们的标注流程采用了多模型共识框架，结合了双前沿大语言模型标注与针对模糊案例的三评委多数投票机制，在人工标注者间一致性上达到了Cohen's Kappa系数0.835。我们发布了：(1) 一个平衡的84K训练集，(2) 一个包含专家人工标注的1K黄金标准评估集，以及(3) [Eva-4B]——一个基于Qwen3-4B微调得到的40亿参数分类器，其宏平均F1分数达到84.9%，优于Claude 4.5、GPT-5.2和Gemini 3 Flash。我们的消融研究证明了多模型共识标注相较于单模型标注的有效性。EvasionBench填补了金融自然语言处理领域的一个重要空白，提供了首个专门针对管理层沟通规避行为的大规模基准。