--- 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 ---

PCA-Bench

Static Badge Static Badge Static Badge Static Badge

*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)