Update README.md

README.md

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-# Combined Combinatorial Optimization Dataset
+---
+language:
+  - "en"
+pretty_name: "LLM Combinatorial Optimization Test Dataset"
+tags:
+  - "reasoning-datasets-competition"
+  - "combinatorial optimization"
+  - "LLM evaluation"
+  - "google OR tools"
+  - "test dataset"
+  - "optimization"
+license: "mit"
+task_categories:
+  - "other"
+---
+
+# LLM Combinatorial Optimization Test Dataset
 
 ## Overview
-This dataset is created for evaluating the effectiveness of Large Language Models (LLMs) on various combinatorial optimization problems. Each instance in the dataset represents a problem instance and includes all the necessary attributes to learn and evaluate problem solutions generated by Google's OR Tools.
-
-## Problem Types and Attributes
-Each instance in the dataset is expected to have the following attributes:
-- **input**: The actual problem data (e.g., graph data for VRP, jobs and machines for JSSP, etc.).
-- **instruction**: A general description of the problem. This attribute contains problem-specific instructions or details (for example, the number of machines and jobs in a jssp problem). The description varies for each problem type.
-- **output_list_of_list**: Expected feasible solution provided as a list of lists.
-- **output_starjob**: Expected solution in a human-readable format. This includes detailed explanations such as how the makespan is calculated in JSSP or how the knapsack capacity is updated step by step.
-
-Additionally, instances are tagged with a specific **problem_type** (e.g., 'vrp', 'jssp', etc.) which may have additional attributes, depending on the specific requirements of the problem type. For example, instances where `problem_type` equals `'vrp'` might include extra attributes relevant only to Vehicle Routing Problems.
-
-## Dataset Statistics
-- **Total Instances**: 150
-
-### Instance Counts by Problem Type
-| Problem Type | Count |
-|--------------|-------|
-| jssp | 30 |
-| binpack | 30 |
-| vrp | 30 |
-| tsp | 30 |
-| knapsack | 30 |
+
+This dataset is created for evaluating the effectiveness of Large Language Models (LLMs) in solving various combinatorial optimization problems. The dataset was generated by combining multiple JSON files containing problem instances from different problem domains. Each instance represents a distinct optimization problem along with its associated solution outputs generated using Google's OR Tools.
+
+## Structure and Attributes
+
+Each instance in this dataset is a JSON object that contains some common attributes along with problem-specific details. The common attributes present in every instance include:
+
+- **input**:  
+  The actual problem data. This attribute represents the raw input needed to describe the optimization problem (e.g., graph information for vehicle routing, job and machine details for job-shop scheduling, etc.).
+
+- **instruction**:  
+  A natural language description of the problem. This attribute captures the general instructions and details (such as number of machines and jobs in a job-shop scheduling problem) that vary for each problem type.
+
+- **output_list_of_list**:  
+  Expected feasible solution represented as a list of lists. This format outlines a structured solution, potentially describing paths, schedules, or allocations.
+
+- **output_starjob**:  
+  A human-readable solution format that includes detailed solution steps. This output explains function calculations and other intermediary steps (for example, how the makespan is computed in a job-shop scheduling problem, or how adjustments are made in knapsack problems).
+
+In addition to these, each instance has the attribute **problem_type**, which indicates the specific category of the combinatorial optimization problem (such as "vrp", "jssp", etc.). Depending on the problem type, instances may also include extra problem-specific attributes that are unique to that type.
+
+For example:
+- **Instances with `problem_type`: "vrp"** might contain additional spatial or routing parameters.
+- **Instances with `problem_type`: "jssp"** include details about the number of machines and jobs, which are also reflected in the `instruction` attribute.
 
 ## Attribute Details
 Below are the attributes extracted from the first instance along with their inferred data types. Note that the dataset may contain additional problem-specific attributes that vary by problem type: