X-LLM

X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

X-LLM converts multi-modalities (images, speech, videos) into foreign languages using X2L interfaces and feed them into a large Language Model (ChatGLM) to accomplish a Multimodal LLM, achieving impressive multimodal chat capabilities.

X-LLM is a general multimodal LLM framework that allows us to incorporate various modalities of information into LLMs, such as (1) non-speech audios, enabling the LLM to have conversations about audios (2) terminal device status information, enabling LLM to control terminal devices, and so on.

X-LLM framework

X-LLM connects multiple pre-trained single-modal encoders (such as ViT-g visual encoder) and large language model ChatGLM, using X2L interfaces. We consider a three-stage training procedure:

Stage 1: Converting Multimodal Information. Convert multimodal information into foreign languages through X2L interfaces, only X2L interfaces are updated
Stage 2: Aligning X2L Representations with the LLM. Inject foreign languages into LLM, only X2L interfaces are updated.
Stage 3: Integrating Multiple Modalities. Integrating multi-modalities, only the adapters in X2L interfaces are updated.

Release

[5/6] We will release the code as soon as possible!

Install
Dataset
Training
Evaluation
Performance

Install

Creating conda environment

conda create -n lavis python=3.8
conda activate lavis

Build from source

git clone https://github.com/phellonchen/X-LLM.git
cd X-LLM
pip install -e .

Dataset

Please see the README_DATA.md for details.

Training

Please see the README_TRAIN_EVAL.md for details.

Evaluation

Please see the README_TRAIN_EVAL.md for details.

Performance

An evaluation dataset with 30 unseen images is constructed: each image is assocaited with three types of instructions: conversation, detailed description and complex reasoning. This leads to 90 new language-image instructions, on which we test X-LLM and GPT-4, and use ChatGPT to rate their responses from score 1 to 10. The summed score and relative score per type is reported. Overall, X-LLM achieves 84.5% relative score compared with GPT-4, indicating the effectinvess of the proposed method in multimodal settings.

Examples

Visual input example, The Forbidden City

Visual input example, Honor of Kings

Acknowledgement

ChatGLM: The codebase we built upon, and our base model ChatGLM-6B that has the amazing Chinese language capabilities!
BLIP2: The model architecture of X-LLM follows BLIP-2. Don't forget to check this great open-source work if you don't know it before!

If you find X-LLM useful for your your research and applications, please cite using this BibTeX:

@article{chen2023x,
  title={X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages},
  author={Chen, Feilong and Han, Minglun and Zhao, Haozhi and Zhang, Qingyang and Shi, Jing and Xu, Shuang and Xu, Bo},
  journal={arXiv preprint arXiv:2305.04160},
  year={2023}
}

phellonchen / X-LLM

readme