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Datasets:
HiThink-Research
/
BizFinBench

Tasks:
Question Answering
Text Classification
Modalities:
Text
Formats:
json
Languages:
Chinese
Size:
1K - 10K
ArXiv:
Tags:
finance
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
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@@ -78,44 +78,11 @@ This dataset contains multiple subtasks, each focusing on a different financial
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  | **Financial Tool Usage** | A financial tool usage dataset evaluating models' ability to understand user queries and appropriately utilize various financial tools (investment analysis, market research, information retrieval, etc.) to solve real-world problems. Tools include calculators, financial encyclopedia queries, search engines, data queries, news queries, economic calendars, and company lookups. Models must accurately interpret user intent, select appropriate tools, input correct parameters, and coordinate multiple tools when necessary. | Tool selection rationality, parameter input accuracy, multi-tool coordination capability | 641 |
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  | **Financial Knowledge QA** | A financial encyclopedia QA dataset assessing models' understanding and response accuracy regarding core financial knowledge, covering key domains: financial fundamentals, markets, investment theories, macroeconomics, etc. | Query comprehension accuracy, knowledge coverage breadth, answer accuracy and professionalism | 990 |
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- ## Performance Leaderboard
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- The models are evaluated across multiple tasks, with results color-coded to represent the top three performers for each task:
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- - 🥇 indicates the top-performing model.
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- - 🥈 represents the second-best result.
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- - 🥉 denotes the third-best performance.
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-
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- | Model | AEA | FNC | FTR | FTU | FQA | FDD | ER | SP | FNER | Average |
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- |--------------------------------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|
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- | **Proprietary LLMs** | | | | | | | | | | |
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- | ChatGPT-o3 | 🥈 86.23 | 61.30 | 🥈 75.36 | 🥇 89.15 | 🥈 91.25 | 🥉 98.55 | 🥉 44.48 | 53.27 | 65.13 | 🥇 73.86 |
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- | ChatGPT-o4-mini | 🥉 85.62 | 60.10 | 71.23 | 74.40 | 90.27 | 95.73 | 🥇 47.67 | 52.32 | 64.24 | 71.29 |
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- | GPT-4o | 79.42 | 56.51 | 🥇 76.20 | 82.37 | 87.79 | 🥇 98.84 | 🥈 45.33 | 54.33 | 65.37 | 🥉 71.80 |
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- | Gemini-2.0-Flash | 🥇 86.94 | 🥉 62.67 | 73.97 | 82.55 | 90.29 | 🥈 98.62 | 22.17 | 🥉 56.14 | 54.43 | 69.75 |
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- | Claude-3.5-Sonnet | 84.68 | 🥈 63.18 | 42.81 | 🥈 88.05 | 87.35 | 96.85 | 16.67 | 47.60 | 63.09 | 65.59 |
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- | **Open Source LLMs** | | | | | | | | | | |
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- | Qwen2.5-7B-Instruct | 73.87 | 32.88 | 39.38 | 79.03 | 83.34 | 78.93 | 37.50 | 51.91 | 30.31 | 56.35 |
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- | Qwen2.5-72B-Instruct | 69.27 | 54.28 | 70.72 | 85.29 | 87.79 | 97.43 | 35.33 | 55.13 | 54.02 | 67.70 |
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- | Qwen2.5-VL-3B | 53.85 | 15.92 | 17.29 | 8.95 | 81.60 | 59.44 | 39.50 | 52.49 | 21.57 | 38.96 |
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- | Qwen2.5-VL-7B | 73.87 | 32.71 | 40.24 | 77.85 | 83.94 | 77.41 | 38.83 | 51.91 | 33.40 | 56.68 |
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- | Qwen2.5-VL-14B | 37.12 | 41.44 | 53.08 | 82.07 | 84.23 | 7.97 | 37.33 | 54.93 | 47.47 | 49.52 |
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- | Qwen2.5-VL-32B | 76.79 | 50.00 | 62.16 | 83.57 | 85.30 | 95.95 | 40.50 | 54.93 | 🥉 68.36 | 68.62 |
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- | Qwen2.5-VL-72B | 69.55 | 54.11 | 69.86 | 85.18 | 87.37 | 97.34 | 35.00 | 54.94 | 54.41 | 67.53 |
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- | Qwen3-1.7B | 77.40 | 35.80 | 33.40 | 75.82 | 73.81 | 78.62 | 22.40 | 48.53 | 11.23 | 50.78 |
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- | Qwen3-4B | 83.60 | 47.40 | 50.00 | 78.19 | 82.24 | 80.16 | 42.20 | 50.51 | 25.19 | 59.94 |
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- | Qwen3-14B | 84.20 | 58.20 | 65.80 | 82.19 | 84.12 | 92.91 | 33.00 | 52.31 | 50.70 | 67.05 |
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- | Qwen3-32B | 83.80 | 59.60 | 64.60 | 85.12 | 85.43 | 95.37 | 39.00 | 52.26 | 49.19 | 68.26 |
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- | Xuanyuan3-70B | 12.14 | 19.69 | 15.41 | 80.89 | 86.51 | 83.90 | 29.83 | 52.62 | 37.33 | 46.48 |
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- | Llama-3.1-8B-Instruct | 73.12 | 22.09 | 2.91 | 77.42 | 76.18 | 69.09 | 29.00 | 54.21 | 36.56 | 48.95 |
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- | Llama-3.1-70B-Instruct | 16.26 | 34.25 | 56.34 | 80.64 | 79.97 | 86.90 | 33.33 | 🥇 62.16 | 45.95 | 55.09 |
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- | Llama 4 Scout | 73.60 | 45.80 | 44.20 | 85.02 | 85.21 | 92.32 | 25.60 | 55.76 | 43.00 | 61.17 |
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- | DeepSeek-V3 (671B) | 74.34 | 61.82 | 72.60 | 🥈 86.54 | 🥉 91.07 | 98.11 | 32.67 | 55.73 | 🥈 71.24 | 71.57 |
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- | DeepSeek-R1 (671B) | 80.36 | 🥇 64.04 | 🥉 75.00 | 81.96 | 🥇 91.44 | 98.41 | 39.67 | 55.13 | 🥇 71.46 | 🥈 73.05 |
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- | QwQ-32B | 84.02 | 52.91 | 64.90 | 84.81 | 89.60 | 94.20 | 34.50 | 🥈 56.68 | 30.27 | 65.77 |
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- | DeepSeek-R1-Distill-Qwen-14B | 71.33 | 44.35 | 16.95 | 81.96 | 85.52 | 92.81 | 39.50 | 50.20 | 52.76 | 59.49 |
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- | DeepSeek-R1-Distill-Qwen-32B | 73.68 | 51.20 | 50.86 | 83.27 | 87.54 | 97.81 | 41.50 | 53.92 | 56.80 | 66.29 |
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  ## 🛠️ Usage
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  ### Quick Start – Evaluate a Local Model
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  ```sh
@@ -218,10 +185,6 @@ The models are evaluated across multiple tasks, with results color-coded to repr
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  | DeepSeek-R1-Distill-Qwen-32B | 73.68 | 51.20 | 50.86 | 83.27 | 87.54 | 97.81 | 41.50 | 53.92 | 56.80 | 66.29 |
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- ## 📚 Example
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- <img src="static/Anomalous Event Attribution.drawio.png" alt="Data Distribution">
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  ## ✒️Citation
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  ```
 
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  | **Financial Tool Usage** | A financial tool usage dataset evaluating models' ability to understand user queries and appropriately utilize various financial tools (investment analysis, market research, information retrieval, etc.) to solve real-world problems. Tools include calculators, financial encyclopedia queries, search engines, data queries, news queries, economic calendars, and company lookups. Models must accurately interpret user intent, select appropriate tools, input correct parameters, and coordinate multiple tools when necessary. | Tool selection rationality, parameter input accuracy, multi-tool coordination capability | 641 |
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  | **Financial Knowledge QA** | A financial encyclopedia QA dataset assessing models' understanding and response accuracy regarding core financial knowledge, covering key domains: financial fundamentals, markets, investment theories, macroeconomics, etc. | Query comprehension accuracy, knowledge coverage breadth, answer accuracy and professionalism | 990 |
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  ## 🛠️ Usage
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+ Please clone the github [link](https://github.com/HiThink-Research/BizFinBench/) to start evaluation
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  ### Quick Start – Evaluate a Local Model
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  ```sh
 
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  | DeepSeek-R1-Distill-Qwen-32B | 73.68 | 51.20 | 50.86 | 83.27 | 87.54 | 97.81 | 41.50 | 53.92 | 56.80 | 66.29 |
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  ## ✒️Citation
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  ```