prometheus-eval/Perception-Collection

Dataset Card

• Homepage: https://kaistai.github.io/prometheus-vision/
• Repository: https://github.com/kaistAI/prometheus-vision
• Paper: https://arxiv.org/abs/2401.06591
• Point of Contact: seongyun@kaist.ac.kr

Dataset summary

Perception Collection is the first multi-modal feedback dataset that could be used to train an evaluator VLM. Perception Collection includes 15K fine-grained criteria that determine the crucial aspect for each instance.

Languages

English

Dataset Structure

• image: The path of the images used for training, consisting of images from the MMMU dataset and COCO 2017 train dataset.
• instruction: The input that is given to the evaluator VLM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
• output: The output that the evaluator VLM should generate. It includes the feedback and score decision divided by a phrase [RESULT].
• orig_instruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
• orig_response: The response to be evaluated.
• orig_reference_answer: A reference answer to the orig_instruction.
• orig_criteria: The score criteria used to evaluate the orig_ response.
• orig_score1_description: A description of when to give a score of 1 to the orig_response.
• orig_score2_description: A description of when to give a score of 2 to the orig_response.
• orig_score3_description: A description of when to give a score of 3 to the orig_response.
• orig_score4_description: A description of when to give a score of 4 to the orig_response.
• orig_score5_description: A description of when to give a score of 5 to the orig_response.
• orig_feedback: A feedback that critiques the orig_response.
• orig_score: An integer between 1 and 5 given to the orig_response.

In our paper, we trained the input using the following prompt format (already processed in the 'instruction'):

###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, image and a score rubric representing an evaluation criterion is given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\"
4. Please do not generate any other opening, closing, and explanations.

###The instruction to evaluate:
{orig_instruction}

###Response to evaluate:
{orig_response}

###Reference Answer (Score 5):
{orig_reference_answer}

###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}

###Feedback: 

Data Splits

name	train
Perception-Collection	150,108

Citation Information

If you find the following dataset helpful, please consider citing our paper!

@misc{lee2024prometheusvision,
      title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation}, 
      author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo},
      year={2024},
      eprint={2401.06591},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}