# EvasionBench: A Large-Scale Benchmark for Detecting Managerial Evasion in Earnings Call Q&A

arXiv: https://arxiv.org/abs/2601.09142 · HTML: https://mlsanity.com/paper/2601.09142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls](https://mlsanity.com/paper/2608.28040/) (2026)

## Abstract (arXiv)

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.