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README.md
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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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## 💡 Highlights
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- 🔥 **Benchmark:** We propose **BizFinBench**, the first evaluation benchmark in the financial domain that integrates business-oriented tasks, covering 5 dimensions and 9 categories. It is designed to assess the capacity of LLMs in real-world financial scenarios.
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- 🔥 **Judge model:** We design a novel evaluation method, i.e., **Iterajudge**, which enhances the capability of LLMs as a judge by refining their decision boundaries in specific financial evaluation tasks.
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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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- 🥇 **Golden** indicates the top-performing model.
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- 🥈 **Light blue** represents the second-best result.
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- 🥉 **Light green** denotes the third-best performance.
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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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## 💡 Highlights
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- 🔥 **Benchmark:** We propose **BizFinBench**, the first evaluation benchmark in the financial domain that integrates business-oriented tasks, covering 5 dimensions and 9 categories. It is designed to assess the capacity of LLMs in real-world financial scenarios.
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- 🔥 **Judge model:** We design a novel evaluation method, i.e., **Iterajudge**, which enhances the capability of LLMs as a judge by refining their decision boundaries in specific financial evaluation tasks.
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