task_categories:text2text-generation size_categories:n<1K format:json modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars arxiv:2502.13092 region:us code
---
task_categories:
- text2text-generation
tags:
- code
size_categories:
- n<1K
---

<h1 align="center">
	🌍 Text2World: Benchmarking Large Language Models for Symbolic World Model Generation<br>
</h1>

This is official dataset for "Text2World: Benchmarking Large Language Models for Symbolic World Model Generation"

# 📊 Data Fields

1. **`file_name`**  
   - **Description**: The name of the PDDL file.  
   - **Example**: `"soartech_soar_grid.pddl"`

2. **`pddl_domain`**  
   - **Description**: The PDDL domain definition, written in the Planning Domain Definition Language (PDDL). It includes predicates and actions that define the rules of the planning domain.  
   - **Example**:  
     ```lisp
     (define (domain grid) ... )
     ```

3. **`batch_id`**  
   - **Description**: An identifier for the batch to which this data belongs. Useful for grouping related data entries.  
   - **Example**: `1`

4. **`id`**  
   - **Type**: `integer`  
   - **Description**: A unique identifier for this specific data entry within its batch.  
   - **Example**: `0`

5. **`tokens`**  
   - **Description**: The number of tokens in the `pddl_domain`. This is often used to measure the size or complexity of the domain definition.  
   - **Example**: `5527`

6. **`corrected_description`**  
   - **Description**: A human-readable description of the PDDL domain, explaining its general purpose, predicates, and actions in plain language.  
   - **Example**:  
     ```markdown
     ### General\nThis domain models a robot navigating a grid environment...
     ```

7. **`pddl_domain_processed`**  
   - **Description**: The processed version of `pddl_domain` has removed the comments.
   -    - **Example**:  
     ```lisp
     (define (domain grid) ... )
     ```

# 📝 Citation

If you find this work useful, please consider citing the following papers:

Text2World: Benchmarking Large Language Models for Symbolic World Model Generation:
```
@misc{hu2025text2worldbenchmarkinglargelanguage,
      title={Text2World: Benchmarking Large Language Models for Symbolic World Model Generation}, 
      author={Mengkang Hu and Tianxing Chen and Yude Zou and Yuheng Lei and Qiguang Chen and Ming Li and Hongyuan Zhang and Wenqi Shao and Ping Luo},
      year={2025},
      eprint={2502.13092},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.13092}, 
}
```
