Duplicate from FutureMa/EvasionBench

Co-authored-by: MaShijian <FutureMa@users.noreply.huggingface.co>

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README.md

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+---
+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
+
+<p align="center">
+  <a href="https://iiiiqiiii.github.io/EvasionBench"><img src="https://img.shields.io/badge/Project-Page-blue?style=for-the-badge" alt="Project Page"></a>
+  <a href="https://huggingface.co/FutureMa/Eva-4B-V2"><img src="https://img.shields.io/badge/🤗-Model-yellow?style=for-the-badge" alt="Model"></a>
+  <a href="https://github.com/IIIIQIIII/EvasionBench"><img src="https://img.shields.io/badge/GitHub-Repo-black?style=for-the-badge&logo=github" alt="GitHub"></a>
+  <a href="https://colab.research.google.com/github/IIIIQIIII/EvasionBench/blob/main/scripts/eva4b_inference.ipynb"><img src="https://img.shields.io/badge/Colab-Quick_Start-F9AB00?style=for-the-badge&logo=googlecolab" alt="Open In Colab"></a>
+  <a href="https://arxiv.org/abs/2601.09142"><img src="https://img.shields.io/badge/arXiv-Paper-red?style=for-the-badge" alt="Paper"></a>
+</p>
+
+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](https://github.com/IIIIQIIII/EvasionBench)
+- **Paper:** [https://arxiv.org/abs/2601.09142](https://arxiv.org/abs/2601.09142)
+- **Point of Contact:** [GitHub Issues](https://github.com/IIIIQIIII/EvasionBench/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
+
+```python
+{
+    "uid": "4addbff893b81f64131fdc712d7a6d9a",
+    "question": "What is the expected margin for Q4?",
+    "answer": "We expect it to be 32%.",
+    "eva4b_label": "direct"
+}
+```
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("FutureMa/EvasionBench")
+```
+
+### Loading from Parquet
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("evasionbench_17k_eva4b_labels_dedup.parquet")
+```
+
+### Quick Start: Inference with Eva-4B-V2
+
+````python
+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](https://colab.research.google.com/github/IIIIQIIII/EvasionBench/blob/main/scripts/eva4b_inference.ipynb).
+
+## 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:
+
+```bibtex
+@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

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