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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- zh |
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- en |
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pretty_name: e |
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configs: |
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- config_name: cn |
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data_files: |
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- split: anomaly_information_tracing |
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path: cn/anomaly_information_tracing_cn.jsonl |
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- split: conterfactual |
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path: cn/conterfactual_cn.jsonl |
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- split: event_logic_reasoning |
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path: cn/event_logic_reasoning_cn.jsonl |
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- split: financial_data_description |
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path: cn/financial_data_description_cn.jsonl |
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- split: financial_multi_turn_perception |
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path: cn/financial_multi-turn_perception_cn.jsonl |
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- split: financial_quantitative_computation |
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path: cn/financial_quantitative_computation_cn.jsonl |
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- split: financial_report_analysis |
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path: cn/financial_report_analysis.jsonl |
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- split: stock_price_predict |
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path: cn/stock_price_predict_cn.jsonl |
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- split: user_sentiment_analysis |
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path: cn/user_sentiment_analysis_cn.jsonl |
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- config_name: en |
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data_files: |
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- split: anomaly_information_tracing |
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path: en/anomaly_information_tracing_en.jsonl |
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- split: conterfactual |
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path: en/conterfactual_en.jsonl |
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- split: event_logic_reasoning |
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path: en/event_logic_reasoning_en.jsonl |
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- split: financial_data_description |
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path: en/financial_data_description_en.jsonl |
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- split: financial_multi_turn_perception |
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path: en/financial_multi-turn_perception_en.jsonl |
|
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- split: financial_quantitative_computation |
|
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path: en/financial_quantitative_computation_en.jsonl |
|
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- split: stock_price_predict |
|
|
path: en/stock_price_predict_en.jsonl |
|
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- split: user_sentiment_analysis |
|
|
path: en/user_sentiment_analysis_en.jsonl |
|
|
--- |
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<p align="center"> |
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<h1 align="center">BizFinBench.v2: A Unified Dual-Mode Bilingual Benchmark for Expert-Level Financial Capability Alignment</h1> |
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<p align="center"> |
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<span class="author-block"> |
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Xin Guo<sup>1,2,*</sup> </a>,</span> |
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<span class="author-block"> |
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Rongjunchen Zhang<sup>1,*,♠</sup></a>, Guilong Lu<sup>1</sup>, Xuntao Guo<sup>1</sup>, Jia Shuai<sup>1</sup>, Zhi Yang<sup>2</sup>, Liwen Zhang<sup>2,♠</sup> |
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</span> |
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</div> |
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<div class="is-size-5 publication-authors" style="margin-top: 10px;"> |
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<span class="author-block"> |
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<sup>1</sup>HiThink Research, <sup>2</sup>Shanghai University of Finance and Economics |
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</span> |
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<br> |
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<span class="author-block"> |
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<sup>*</sup>Co-first authors, <sup>♠</sup>Corresponding author, zhangrongjunchen@myhexin.com,zhang.liwen@shufe.edu.cn |
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</span> |
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</div> |
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</p> |
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<p> |
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📖<a href="https://arxiv.org/abs/2601.06401">Paper</a> |🏠<a href="https://hithink-research.github.io/BizFinBench.v2/">Homepage</a> |
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</p> |
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<div align="center"></div> |
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<p align="center"> |
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**BizFinBench.v2** is the secend release of [BizFinBench](https://github.com/HiThink-Research/BizFinBench). It is built entirely on real-world user queries from Chinese and U.S. equity markets. It bridges the gap between academic evaluation and actual financial operations. |
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<img src="static/score_sequence.png" alt="Evaluation Result"> |
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### 🌟 Key Features |
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* **Authentic & Real-Time:** 100% derived from real financial platform queries, integrating online assessment capabilities. |
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* **Expert-Level Difficulty:** A challenging dataset of **29,578 Q&A pairs** requiring professional financial reasoning. |
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* **Comprehensive Coverage:** Spans **4 core business scenarios**, 8 fundamental tasks, and 2 online tasks. |
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### 📊 Key Findings |
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* **High Difficulty:** Even **ChatGPT-5** achieves only 61.5% accuracy on main tasks, highlighting a significant gap vs. human experts. |
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* **Online Prowess:** **DeepSeek-R1** outperforms all other commercial LLMs in dynamic online tasks, achieving a total return of 13.46% with a maximum drawdown of -8%. |
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## 📢 News |
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- 🚀 [06/01/2026] TBD |
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## 📕 Data Distrubution |
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BizFinBench.v2 contains multiple subtasks, each focusing on a different financial understanding and reasoning ability, as follows: |
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### Distribution Visualization |
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<div align="center"> |
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<img src="static/distribution.png" alt="Data Distribution" width="600"> |
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</div> |
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### Detailed Statistics |
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| Scenarios | Tasks | Avg. Input Tokens | # Questions | |
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|:---|:---|---:|---:| |
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| **Business Information Provenance** | Anomaly Information Tracing | 8,679 | 4,000 | |
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| | Financial Multi-turn Perception | 10,361 | 3,741 | |
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| | Financial Data Description | 3,577 | 3,837 | |
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| **Financial Logic Reasoning** | Financial Quantitative Computation | 1,984 | 2,000 | |
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| | Event Logic Reasoning | 437 | 4,000 | |
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| | Counterfactual Inference | 2,267 | 2,000 | |
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| **Stakeholder Feature Perception** | User Sentiment Analysis | 3,326 | 4,000 | |
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| | Financial Report Analysis | 19,681 | 2,000 | |
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| **Real-time Market Discernment** | Stock Price Prediction | 5,510 | 4,000 | |
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| | Portfolio Asset Allocation | — | — | |
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| **Total** | **—** | **—** | **29,578** | |
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## ✒️Citation |
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``` |
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Coming Soon |
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``` |
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## 📄 License |
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  **Usage and License Notices**: The data and code are intended and licensed for research use only. |
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License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use |
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## 💖 Acknowledgement |
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* Special thanks to Ning Zhang, Siqi Wei, Kai Xiong, Kun Chen and colleagues at HiThink Research's data team for their support in building BizFinBench.v2. |
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