Update README.md

README.md

@@ -25,12 +25,12 @@ CCR-Bench is designed to assess LLMs’ ability to follow complex instructions t
 
 CCR_Bench covers 3 core compents:
 
-* **Complex Content-Format Constraints**: Existing benchmarks have improved model performance in generating structured text and adhering to simple constraints. Building upon this, CCR-Bench introduces a set of tightly coupled “content-format” instructions, in which the output format itself constitutes a critical component of the content logic. These instructions require models to generate specific content while strictly adhering to predefined format specifications, where the format itself is an integral component of the content’s logical structure. This dimension aims to rigorously assess models’ precision in complying with complex, multi-layered constraints, especially their ability to integrate formatting requirements into the overall logic of content generation.
+* **Complex Content-Format Constraints**: Existing benchmarks have improved model performance in generating structured text and adhering to simple constraints. Building upon this, CCR-Bench introduces a set of tightly coupled “content-format” instructions, in which the output format itself constitutes a critical component of the content logic. These instructions require models to generate specific content while strictly adhering to predefined format specifications, where the format itself is an integral component of the content’s logical structure. This dimension aims to rigorously assess models’ precision in complying with complex, multi-layered constraints, especially their ability to integrate formatting requirements into the overall logic of content generation. 
 
-* **Logical Workflow Control**: To complement the limited scope of current instruction-following benchmarks in evaluating complex task decomposition, conditional reasoning, stepwise planning, and tool usage, we design tasks that demand multi-turn interaction, procedural planning, and state tracking. This dimension evaluates a model’s capacity to transition from passively following instructions to actively orchestrating and executing complex workflows.
+* **Logical Workflow Control**: To complement the limited scope of current instruction-following benchmarks in evaluating complex task decomposition, conditional reasoning, stepwise planning, and tool usage, we design tasks that demand multi-turn interaction, procedural planning, and state tracking. This dimension evaluates a model’s capacity to transition from passively following instructions to actively orchestrating and executing complex workflows. This datasets contains complex instruction sets from 9 small scenarios, which are customer service, data flow, flight tickets, maze, online game, printer assistant, real estate, tree painter, world cup simulator.
 
 * **Industrial Scenario Application**: This dimension synthesizes the capabilities assessed in the previous two components by introducing comprehensive tasks situated in realistic industrial contexts. These tasks involve both
-content-format constraints and logical reasoning, while also being tightly integrated with domain-specific requirements. The goal is to evaluate the practical utility and robustness of LLMs under conditions that approximate real-world industrial deployments.
+content-format constraints and logical reasoning, while also being tightly integrated with domain-specific requirements. The goal is to evaluate the practical utility and robustness of LLMs under conditions that approximate real-world industrial deployments. The dataset contains complex instruction sets for medical-industrial scenarios, such as pre-consultation and healthy diet guidance.
 
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