---
task_categories:
- visual-question-answering
language:
- en
tags:
- Vision
- food
- recipe
configs:
- config_name: Food101
  data_files:
  - split: test
    path: food101/data-*.arrow
- config_name: FoodSeg103
  data_files:
  - split: test
    path: foodseg103/data-*.arrow
- config_name: Nutrition5K
  data_files:
  - split: test
    path: nutriton50k/data-*.arrow
- config_name: Recipe1M
  data_files:
  - split: test
    path: food_eval_multitask_v2/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](https://huggingface.co/papers/2411.19930).

The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains/edit/main/README.md)

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.

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
</p>

## How to use

```python
from datasets import load_dataset

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

# Load the dataset for the chosen task
data = load_dataset('AdaptLLM/food-vision-language-tasks', task_name, split='test')
```

## Citation
If you find our work helpful, please cite us.

AdaMLLM
```bibtex
@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](https://huggingface.co/papers/2309.09530) (ICLR 2024)
```bibtex
@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}
}
```