metadata

task_categories:
  - visual-question-answering
language:
  - en
tags:
  - Vision
  - food
  - recipe
configs:
  - config_name: Recipe1M
    data_files:
      - split: test
        path: food_eval_multitask_v2/data-*.arrow
  - config_name: Nutrition5K
    data_files:
      - split: test
        path: nutrition50k/data-*.arrow
  - config_name: Food101
    data_files:
      - split: test
        path: food101/data-*.arrow
  - config_name: FoodSeg103
    data_files:
      - split: test
        path: foodseg103/data-*.arrow

Adapting Multimodal Large Language Models to Domains via Post-Training

This repos contains the food visual instruction tasks for evaluating MLLMs in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.

The main project page is: Adapt-MLLM-to-Domains

We investigate domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. (1) Data Synthesis: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs. (2) Training Pipeline: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. (3) Task Evaluation: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.

How to use

from datasets import load_dataset

# Choose the task name from the list of available tasks
task_name = 'FoodSeg103' # Options: 'Food101', 'FoodSeg103', 'Nutrition5K', 'Recipe1M'

# Load the dataset for the chosen task
data = load_dataset('AdaptLLM/food-VQA-benchmark', task_name, split='test')

Citation

If you find our work helpful, please cite us.

AdaMLLM

@article{adamllm,
  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
  journal={arXiv preprint arXiv:2411.19930},
  year={2024}
}

AdaptLLM (ICLR 2024)

@inproceedings{
adaptllm,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}