Update README.md

README.md

@@ -34,5 +34,52 @@ tags:
 pretty_name: humanevalv
 ---
 
-# HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks
+## HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks
+<p align="left">
+    <a href="https://humaneval-v.github.io">🏠 Home Page</a> •
+    <a href="https://github.com/HumanEval-V/HumanEval-V-Benchmark">💻 GitHub Repository </a> •
+    <a href="https://humaneval-v.github.io/#leaderboard">🏆 Leaderboard</a> •
+    <a href="">🤗 Dataset Viewer</a> •
+    <a href="">📄 Paper </a>
+</p>
 
+HumanEval-V includes 108 carefully crafted, entry-level Python coding tasks. LMMs are required to complete the code solution based on the provided visual context and a predefined Python function signature outlining the task requirements. Every task is equipped with meticulously handcrafted test cases for execution-based pass@k evaluation.
+
+## Dataset Structure
+Each task in the dataset consists of the following fields:
+
+- **qid**: Unique identifier for each coding task (e.g., _q1_, with mutated versions like _q1-2_, _q1-3_).
+- **image**: A single image containing the essential visual context necessary to solve the task.
+- **function_signature**: Includes the problem description, necessary imports, and the function signature that the LMMs must complete.
+- **test_script**: Test cases used to validate the correctness of the generated code.
+- **ground_truth_solution**: Expert-crafted solutions provided for reference but not used during the evaluation process.
+- **image_description**: Human-labeled descriptions of the images, used for experimental analysis (not part of the benchmark evaluation).
+
+## Prompt Format
+Each task is formatted with a clear instruction and provided function signature to guide the model in generating the code solution:
+
+````markdown
+**Instructions:**
+You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+Please complete the function based on the provided image and code context. Return the complete solution, including the function signature, in a single response, formatted within a Python code block.
+
+**Code Context:**
+```python
+{code_context}
+```
+````
+
+After the LMM generates a response, the code solution is extracted and validated using the following process:
+- Extraction of content within the code block.
+- Parsing the generated code to detect imports, class definitions, and functions using an Abstract Syntax Tree (AST) parser.
+- Concatenation of these components to form the final predicted solution, which is then tested for correctness.
+- Generated code solution is evaluated through an execution-based metric, specifically **pass@k**.
+
+
+## Usage
+You can easily load the dataset using the Hugging Face `datasets` library.
+
+```python
+from datasets import load_dataset
+humaneval_v = load_dataset("HumanEval-V/HumanEval-V-Benchmark", split="test")
+```