EvasionBench: Detecting Evasive Answers in Financial Q&A via Multi-Model Consensus and LLM-as-Judge

• URL: http://arxiv.org/abs/2601.09142v1
• Date: Wed, 14 Jan 2026 04:26:43 GMT
• Title: EvasionBench: Detecting Evasive Answers in Financial Q&A via Multi-Model Consensus and LLM-as-Judge
• Authors: Shijian Ma, Yan Lin, Yi Yang,
• Abstract summary: We introduce EvasionBench, comprising 30,000 training samples and 1,000 human-annotated test samples.<n>We mine boundary cases where two strong annotators conflict, using a judge to resolve labels.<n>Our trained model Eva-4B (4B parameters) achieves 81.3 percent accuracy, outperforming its base by 25 percentage points.
• Score: 8.50639201265868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting evasive answers in earnings calls is critical for financial transparency, yet progress is hindered by the lack of large-scale benchmarks. We introduce EvasionBench, comprising 30,000 training samples and 1,000 human-annotated test samples (Cohen's Kappa 0.835) across three evasion levels. Our key contribution is a multi-model annotation framework leveraging a core insight: disagreement between frontier LLMs signals hard examples most valuable for training. We mine boundary cases where two strong annotators conflict, using a judge to resolve labels. This approach outperforms single-model distillation by 2.4 percent, with judge-resolved samples improving generalization despite higher training loss (0.421 vs 0.393) - evidence that disagreement mining acts as implicit regularization. Our trained model Eva-4B (4B parameters) achieves 81.3 percent accuracy, outperforming its base by 25 percentage points and approaching frontier LLM performance at a fraction of inference cost.

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