README.md

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - finance
  - earnings-calls
  - evasion-detection
  - nlp
pretty_name: EvasionBench
size_categories:
  - 10K<n<100K

EvasionBench

EvasionBench is a benchmark dataset for detecting evasive answers in earnings call Q&A sessions. The task is to classify how directly corporate management addresses questions from financial analysts.

Dataset Description

• Repository: https://github.com/IIIIQIIII/EvasionBench
• Paper: https://arxiv.org/abs/2601.09142
• Point of Contact: GitHub Issues

Dataset Summary

This dataset contains 16,726 question-answer pairs from earnings call transcripts, each labeled with one of three evasion levels. The labels were generated using the Eva-4B-V2 model, a fine-tuned classifier specifically trained for financial discourse evasion detection.

Supported Tasks

• Text Classification: Classify the directness of management responses to analyst questions.

Languages

English

Dataset Structure

Data Fields

Field	Type	Description
uid	string	Unique identifier for each sample
question	string	Analyst's question during the earnings call
answer	string	Management's response to the question
eva4b_label	string	Evasion label: direct, intermediate, or fully_evasive

Label Definitions

Label	Definition	Description
direct	The core question is directly and explicitly answered	Clear figures, "Yes/No" stance, or direct explanations
intermediate	The response provides related context but sidesteps the specific core	Hedging, providing a range instead of a point, or answering adjacent topics
fully_evasive	The question is ignored, explicitly refused, or the response is entirely off-topic	Explicit refusal, total redirection, or irrelevant responses

Data Statistics

Metric	Value
Total Samples	16,726
Direct	8,749 (52.3%)
Intermediate	7,359 (44.0%)
Fully Evasive	618 (3.7%)

Example

{
    "uid": "4addbff893b81f64131fdc712d7a6d9a",
    "question": "What is the expected margin for Q4?",
    "answer": "We expect it to be 32%.",
    "eva4b_label": "direct"
}

Usage

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("FutureMa/EvasionBench")

Loading from Parquet

import pandas as pd

df = pd.read_parquet("evasionbench_17k_eva4b_labels_dedup.parquet")

Quick Start: Inference with Eva-4B-V2

from datasets import load_dataset
from transformers import pipeline

# Load dataset
dataset = load_dataset("FutureMa/EvasionBench")

# Load model using text-generation pipeline
pipe = pipeline("text-generation", model="FutureMa/Eva-4B-V2", device_map="auto")

# Get a sample
sample = dataset["train"][0]
question = sample["question"]
answer = sample["answer"]

# Prepare prompt
prompt = f"""You are a financial analyst. Your task is to Detect Evasive Answers in Financial Q&A

Question: {question}
Answer: {answer}

Response format:
```json
{{"label": "direct|intermediate|fully_evasive"}}
```

Answer in ```json content, no other text"""

# Run inference
result = pipe(prompt, max_new_tokens=64, do_sample=False)
print(result[0]["generated_text"])

For a complete inference example with batch processing and evaluation, see our Colab notebook.

Dataset Creation

Source Data

The question-answer pairs are derived from publicly available earnings call transcripts.

Annotation Process

Labels were generated using Eva-4B-V2, a 4B parameter model fine-tuned specifically for evasion detection in financial discourse. Eva-4B-V2 achieves 84.9% Macro-F1 on the EvasionBench evaluation set, outperforming frontier LLMs including Claude Opus 4.5 and Gemini 3 Flash.

Considerations for Using the Data

Social Impact

This dataset can be used to:

• Improve transparency in corporate communications
• Assist financial analysts in identifying evasive responses
• Support research in financial NLP and discourse analysis

Limitations

• Labels are model-generated (Eva-4B-V2) rather than human-annotated
• The dataset reflects the distribution of evasion patterns in the source data
• Performance may vary across different industries or time periods

Citation

If you use this dataset, please cite:

@misc{ma2026evasionbenchlargescalebenchmarkdetecting,
  title={EvasionBench: A Large-Scale Benchmark for Detecting Managerial Evasion in Earnings Call Q&A},
  author={Shijian Ma and Yan Lin and Yi Yang},
  year={2026},
  eprint={2601.09142},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2601.09142}
}

License

Apache 2.0