README.md

---
dataset_info:
- config_name: Autonomous Driving
  features:
  - name: domain
    dtype: string
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: actions
    sequence: string
  - name: answer_index
    dtype: int64
  - name: reason
    dtype: string
  - name: key_concept
    sequence: string
  - name: question_prompt
    dtype: string
  - name: answer_with_reason
    dtype: string
  - name: full_meta_data_json
    dtype: string
  splits:
  - name: test_open
    num_bytes: 134659773
    num_examples: 100
  - name: test_closed
    num_bytes: 67549223
    num_examples: 150
  download_size: 270416985
  dataset_size: 202208996
- config_name: Domestic Robot
  features:
  - name: domain
    dtype: string
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: actions
    sequence: string
  - name: answer_index
    dtype: int64
  - name: reason
    dtype: string
  - name: key_concept
    sequence: string
  - name: question_prompt
    dtype: string
  - name: answer_with_reason
    dtype: string
  - name: full_meta_data_json
    dtype: string
  splits:
  - name: test_open
    num_bytes: 91702060
    num_examples: 100
  - name: test_closed
    num_bytes: 177827577
    num_examples: 200
  download_size: 105390299
  dataset_size: 269529637
- config_name: Open-World Game
  features:
  - name: domain
    dtype: string
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: actions
    sequence: string
  - name: answer_index
    dtype: int64
  - name: reason
    dtype: string
  - name: key_concept
    sequence: string
  - name: question_prompt
    dtype: string
  - name: answer_with_reason
    dtype: string
  - name: full_meta_data_json
    dtype: string
  splits:
  - name: test_open
    num_bytes: 16139511
    num_examples: 117
  - name: test_closed
    num_bytes: 19069366
    num_examples: 141
  download_size: 34988721
  dataset_size: 35208877
configs:
- config_name: Autonomous Driving
  data_files:
  - split: test_open
    path: Autonomous Driving/test_open-*
  - split: test_closed
    path: Autonomous Driving/test_closed-*
- config_name: Domestic Robot
  data_files:
  - split: test_open
    path: Domestic Robot/test_open-*
  - split: test_closed
    path: Domestic Robot/test_closed-*
- config_name: Open-World Game
  data_files:
  - split: test_open
    path: Open-World Game/test_open-*
  - split: test_closed
    path: Open-World Game/test_closed-*
license: apache-2.0
task_categories:
- multiple-choice
- visual-question-answering
language:
- en
pretty_name: PCA-Bench
---


<h1 align="center">PCA-Bench</h1>

<p align="center">

<a href="https://github.com/pkunlp-icler/PCA-EVAL">
<img alt="Static Badge" src="https://img.shields.io/badge/Github-Online-white">

<a href="https://github.com/pkunlp-icler/PCA-EVAL/blob/main/PCA_Bench_Paper.pdf">
<img alt="Static Badge" src="https://img.shields.io/badge/Paper-PCABench-red">

<a href="https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1">
<img alt="Static Badge" src="https://img.shields.io/badge/HFDataset-PCABenchV1-yellow">
</a>

<a href="https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV">
<img alt="Static Badge" src="https://img.shields.io/badge/Leaderboard-Online-blue">
</a>
</p>




*PCA-Bench is an innovative benchmark for evaluating and locating errors in Multimodal LLMs when conducting embodied decision making tasks, specifically focusing on perception, cognition, and action.*


## Release
- [2024.02.15] [PCA-Bench-V1](https://github.com/pkunlp-icler/PCA-EVAL) is released. We release the open and closed track data in [huggingface](https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1). We also set an online [leaderboard ](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV) accepting users' submission.
- [2023.12.15] [PCA-EVAL](https://arxiv.org/abs/2310.02071) is accepted to Foundation Model for Decision Making Workshop @NeurIPS 2023. PCA-Evaluation tool is released in github.

## Leaderboard
[Leaderboard with Full Metrics](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV)



## Submit Results

📢 For close track evaluaiton and PCA-Evaluation, please follow [this file](https://github.com/pkunlp-icler/PCA-EVAL/blob/main/pca-eval/results/chatgpt_holmes_outputs/Autonomous%20Driving.json) to organize your model output. Submit **Six JSON files** from different domains and different tracks, along with your **model name** and **organization** to us via [email](mailto:leo.liang.chen@stu.pku.edu.cn). Ensure you use the dataset's provided prompt as the default input for fair comparison.

We will send the PCA-Eval results of your model to you and update the leaderboard.

We provide sample code to get the six json files. User only needs to add your model inference code:
```python
# Sample code for PCA-Eval
from datasets import load_dataset
from tqdm import tqdm
import json
import os

def YOUR_INFERENCE_CODE(prompt,image):
    """Simple single round multimodal conversation call.
    """
    response = YOUR_MODEL.inference(prompt,image)
    return response

output_path = "./Results-DIR-PATH/"
os.mkdir(output_path)

dataset_ad = load_dataset("PCA-Bench/PCA-Bench-V1","Autonomous Driving")
dataset_dr = load_dataset("PCA-Bench/PCA-Bench-V1","Domestic Robot")
dataset_og = load_dataset("PCA-Bench/PCA-Bench-V1","Open-World Game")

test_dataset_dict = {"Autonomous-Driving":dataset_ad,"Domestic-Robot":dataset_dr,"Open-World-Game":dataset_og}
test_split = ["test_closed","test_open"]
test_domain = list(test_dataset_dict.keys())

for domain in test_domain:
  for split in test_split:
    print("testing on %s:%s"%(domain,split))

    prediction_results = []
    output_filename = output_path+"%s-%s.json"%(domain,split)
    prompts = test_dataset_dict[domain][split]['question_prompt']
    images = test_dataset_dict[domain][split]['image']

    for prompt_id in tqdm(range(len(prompts))):
        user_inputs = prompts[prompt_id] # do not change the prompts for fair comparison
        index = prompt_id
        image = images[prompt_id]

        outputs = YOUR_INFERENCE_CODE(user_inputs,image)

        prediction_results.append({
            'prompt': user_inputs,
            'model_output': outputs,
            'index': index,
        })

    with open(output_filename, 'w') as f:
        json.dump(prediction_results, f, indent=4)

# submit the 6 json files in the output_path to our email
```

You could also simply compute the multiple-choice accuracy locally as a comparison metric in your own experiments. However, in the online leaderboard, we only consider the average action score and Genuine PCA score when ranking models.


For more information, refer to the offical [github repo](https://github.com/pkunlp-icler/PCA-EVAL)