---
license: apache-2.0
---

# JDERW: A Benchmark for Evaluating World Models in Large Language Models (LLMs)

## Overview
JDERW (Japanese Dataset for Evaluating Reasoning with World Models) is a benchmark dataset designed to assess the ability of Large Language Models (LLMs) to understand and reason about real-world phenomena and common sense. It includes **103 questions** categorized into six reasoning types:

1. **Causal Reasoning** (e.g., Why does it snow in winter?)
2. **Temporal Reasoning** (e.g., What happens when you leave a hot coffee out?)
3. **Spatial Reasoning** (e.g., What happens to a ball placed on a slope?)
4. **Abstract Concept Reasoning** (e.g., What is happiness?)
5. **Common Sense Reasoning** (e.g., How should you cross the road?)
6. **Planning Reasoning** (e.g., How do you make curry?)

This dataset enables a detailed evaluation of LLMs’ strengths and weaknesses in **world model comprehension**, paving the way for improvements in model development.

## Dataset Structure
Each sample in JDERW consists of:
- **situation**: Context or scenario setting
- **question**: The question to be answered
- **answer**: A reference correct answer
- **reasoning**: Explanation for the answer
- **eval aspect**: Evaluation criteria
- **genre**: The type of reasoning involved

## Usage
To use the JDERW dataset for inference, you can utilize the provided script. Below is an example usage with a Hugging Face model.

### Installation
Ensure you have the required dependencies installed:
```bash
pip install torch datasets transformers
```

### Running Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

def main(model_name):
    ds = load_dataset("DeL-TaiseiOzaki/JDERW")

    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(model_name).eval()

    def pred(example):
        situation = example["situation"]
        question = example["question"]
        prompt = f"{situation}\n{question}"
        response, _ = model.chat(tokenizer, prompt, history=None)
        example[model_name] = response
        return example

    ds = ds.map(pred, batched=False)
    ds.to_csv(f"{model_name.replace('/', '-')}.csv", index=False)

if __name__ == "__main__":
    main("<HuggingFace Model ID>")
```

Replace `<HuggingFace Model ID>` with the ID of the model you wish to use.

## Benchmarking Results
JDERW has been used to evaluate various LLMs, and the results show distinct strengths and weaknesses across different reasoning categories. Some key findings include:
- **Llama-3.3-70B-Instruct** excels in **temporal** and **abstract** reasoning.
- **GPT-4o** and **Claude-3-5-Sonnet** perform well in **planning** and **common sense** reasoning.
- Most models struggle with **abstract concept reasoning**.

| Model | Causal | Spatial | Temporal | Planning | Common Sense | Abstract Concept |
|--------|--------|--------|--------|--------|--------|--------|
| Llama-3.3-70B-Instruct | 4.032 | 3.914 | 4.214 | 3.867 | 4.057 | 3.667 |
| GPT-4o | 3.903 | 4.114 | 4.071 | 4.200 | 3.857 | 2.667 |
| Claude-3-5-Sonnet | 4.000 | 3.743 | 3.857 | 4.000 | 4.000 | 3.333 |

These findings highlight the importance of **evaluating LLMs beyond simple accuracy metrics** to understand how well they internalize world models.

## Future Directions
1. **Expanding the dataset**: Increasing the number of questions to cover more diverse real-world scenarios.
2. **Human comparison**: Comparing LLM performance with human responses to better assess gaps in world modeling.
3. **Exploring new categories**: Investigating additional reasoning dimensions beyond the six currently defined.
4. **Improving evaluation metrics**: Refining assessment criteria to provide deeper insights into LLM capabilities.

## Citation
If you use JDERW in your research, please cite the following paper:
```
@article{JDERW2024,
  author    = {Taisei Ozaki and Takumi Matsushita and Tsuyoshi Miura},
  title     = {JDERW: A Benchmark for Evaluating World Models in Large Language Models},
  journal   = {arXiv preprint arXiv:XXXX.XXXX},
  year      = {2024}
}
```

## Acknowledgments
This research is supported by **Osaka Metropolitan University**, **Institute of Science Tokyo**, and **The University of Tokyo**.