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README.md

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 📖<a href="https://arxiv.org/abs/2505.19457">Paper</a> |🐙<a href="https://github.com/HiThink-Research/BizFinBench/">Github</a></h3>|🤗<a href="https://huggingface.co/datasets/HiThink-Research/BizFinBench">Huggingface</a></h3>
 
-In recent years, multimodal benchmarks for general domains have guided the rapid development of multimodal models on general tasks. However, the financial field has its peculiarities. It features unique graphical images (e.g., candlestick charts, technical indicator charts) and possesses a wealth of specialized financial knowledge (e.g., futures, turnover rate).
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-Large language models excel across general tasks, yet judging their reliability in logic‑heavy, precision‑critical domains such as finance, law and healthcare is still difficult. To address this challenge, we propose **BizFinBench**, the first benchmark grounded in real-world financial applications. BizFinBench consists of **6,781** well-annotated queries in Chinese, covering five dimensions: numerical calculation, reasoning, information extraction, prediction recognition and knowledge‐based question answering, which are mapped to nine fine-grained categories.
+Large language models excel in general tasks, yet assessing their reliability in logic‑heavy, precision‑critical domains like finance, law, and healthcare remains challenging. To address this, we introduce BizFinBench, the first benchmark specifically designed to evaluate LLMs in real-world financial applications. BizFinBench comprises over 100,000+ bilingual (English & Chinese) financial questions, each rooted in real-world business scenarios. The first public release, BizFinBench.v1, delivers 6,781 well annotated Chinese queries, spanning five dimensions: numerical calculation, reasoning, information extraction, prediction recognition, and knowledge-based question answering, grouped into nine fine-grained categories. The benchmark includes both objective and subjective metrics. We also introduce Iterajudge, a novel LLM evaluation method that reduces bias when LLMs serve as evaluators in objective metrics. We benchmark 25 models, including both proprietary and open-source systems. Extensive experiments show that no model dominates across all tasks. Our evaluation reveals distinct capability patterns: (1) In Numerical Calculation, Claude-3.5-Sonnet (63.18) and DeepSeek-R1 (64.04) lead, while smaller models like Qwen2.5-VL-3B (15.92) lag significantly; (2) In Reasoning, proprietary models dominate (ChatGPT-o3: 83.58, Gemini-2.0-Flash: 81.15), with open-source models trailing by up to 19.49 points; (3) In Information Extraction, the performance spread is the largest, with DeepSeek-R1 scoring 71.46, while Qwen3-1.7B scores 11.23; (4) In Prediction Recognition, performance variance is minimal, with top models scoring between 39.16 and 50.00. We find that while current LLMs handle routine finance queries competently, they struggle with complex scenarios requiring cross-concept reasoning. BizFinBench offers a rigorous, business-aligned benchmark for future research. The code and dataset are available at https://github.com/HiThink-Research/BizFinBench.
 
 ## 📢 News 
 - 🚀 [16/05/2025] We released <strong>BizFinBench</strong> benchmark (V1), the first benchmark grounded in real-world financial applications.