Create README.md

README.md

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+---
+license: cc-by-4.0
+language:
+- zh
+tags:
+- finance
+pretty_name: BizFinBench
+size_categories:
+- 10M<n<100M
+---
+# BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs
+
+## 📌 Overview
+
+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.
+
+This dataset contains multiple subtasks, each focusing on a different financial understanding and reasoning ability, as follows:
+
+| Dataset | Description | Evaluation Dimensions | Volume |  
+| --- | --- | --- | --- |  
+| **Anomalous Event Attribution** | A financial anomaly attribution evaluation dataset assessing models' ability to trace stock fluctuations based on given information (e.g., timestamps, news articles, financial reports, and stock movements). | Causal consistency, information relevance, noise resistance | 1,064 |  
+| **Financial Numerical Computation** | A financial numerical computation dataset evaluating models' ability to perform accurate numerical calculations in financial scenarios, including interest rate calculations, gain/loss computations, etc. | Calculation accuracy, unit consistency | 581 |  
+| **Financial Time Reasoning** | A financial temporal reasoning evaluation dataset assessing models' ability to comprehend and reason about time-based financial events, such as "the previous trading day" or "the first trading day of the year." | Temporal reasoning correctness | 514 |  
+| **Financial Data Description** | A financial data description evaluation dataset measuring models' ability to analyze and describe structured/unstructured financial data, e.g., "the stock price first rose to XX before falling to XX." | Trend accuracy, data consistency | 1,461 |  
+| **Stock Price Prediction** | A stock price movement prediction dataset evaluating models' ability to forecast future stock price trends based on historical data, financial indicators, and market news. | Trend judgment, causal rationality | 497 |  
+| **Financial Named Entity Recognition** | A financial named entity recognition dataset assessing models' ability to identify entities (Person, Organization, Market, Location, Financial Products, Date/Time) in short/long financial news. | Recognition accuracy, entity category correctness | 433 |  
+| **Emotion_Recognition** | A financial sentiment recognition dataset evaluating models' ability to discern nuanced user emotions in complex financial market environments. Inputs include multi-dimensional data such as market conditions, news, research reports, user holdings, and queries, covering six emotion categories: optimism, anxiety, pessimism, excitement, calmness, and regret. | Emotion classification accuracy, implicit information extraction and reasoning correctness | 600 |  
+| **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 |  
+| **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 |