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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2601.09142 |
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| _version_ | 1866911421606920192 |
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| author | Ma, Shijian Lin, Yan Yang, Yi |
| author_facet | Ma, Shijian Lin, Yan Yang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09142 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EvasionBench: A Large-Scale Benchmark for Detecting Managerial Evasion in Earnings Call Q&A Ma, Shijian Lin, Yan Yang, Yi Machine Learning Computation and Language 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. |
| title | EvasionBench: A Large-Scale Benchmark for Detecting Managerial Evasion in Earnings Call Q&A |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2601.09142 |