Update README.md

README.md

@@ -75,4 +75,9 @@ This dataset contains multiple subtasks, each focusing on a different financial
 | **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 |
+| **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 |
+
+## 💡 Highlights
+- 🔥  **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.
+- 🔥  **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.
+- 🔥  **key insights:** We conduct a comprehensive evaluation with **25 LLMs** based on BizFinBench, uncovering key insights into their strengths and limitations in financial applications.