MultiModal-GPT: A Vision and Language Model for Dialogue with Humans (2305.04790v3)

Abstract: We present a vision and LLM named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-efficiently fine-tuned from OpenFlamingo, with Low-rank Adapter (LoRA) added both in the cross-attention part and the self-attention part of the LLM. We first construct instruction templates with vision and language data for multi-modality instruction tuning to make the model understand and follow human instructions. We find the quality of training data is vital for the dialogue performance, where few data containing short answers can lead the model to respond shortly to any instructions. To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly. The joint training of language-only and visual-language instructions with the \emph{same} instruction template effectively improves dialogue performance. Various demos show the ability of continuous dialogue of MultiModal-GPT with humans. Code, dataset, and demo are at https://github.com/open-mmlab/Multimodal-GPT

• The paper introduces MultiModal-GPT, which integrates vision and language modalities using an OpenFlamingo base with CLIP-inspired encoders and LLaMA decoders enhanced by LoRA.
• The methodology employs a unified instruction template combining unimodal and multimodal data to boost comprehensive dialogue and context handling.
• Experimental results demonstrate strong performance in OCR, object counting, recipe generation, and recommendations, emphasizing the model's versatility in real-world tasks.

MultiModal-GPT: A Vision and LLM for Dialogue with Humans

The paper under review presents an advanced artificial intelligence model, MultiModal-GPT, which integrates vision and language modalities for engaging in dialogue with humans. Developed by researchers associated with the Shanghai AI Laboratory, The University of Hong Kong, and Tianjin University, MultiModal-GPT is designed to follow diverse instructions, ranging from generating detailed captions to comprehensively answering user inquiries.

Model Architecture and Components

The architecture of MultiModal-GPT builds upon the OpenFlamingo framework. This model incorporates a vision encoder inspired by CLIP, a perceiver resampler for spatial feature extraction, and LLaMA as the language decoder. The language decoder is enhanced with gated cross-attention mechanisms allowing effective encoding of visual features into textual representations.

The integration of Low-rank Adapter (LoRA) within both gated cross-attention and self-attention components optimizes the fine-tuning phase, enhancing the model's efficiency without the need for extensive computational resources. By freezing the core components of OpenFlamingo, the researchers focus on fine-tuning using LoRA to adapt the model successfully to multimodal inputs.

Unified Instruction Template

A significant aspect of this paper is the introduction of a unified instruction template for training data, comprising unimodal linguistic data and multimodal vision-and-language data. The unified approach leverages the complementary strengths of both modalities, fostering a more profound understanding of underlying concepts and enabling synchronization of instruction-following capabilities across diverse tasks.

Importance of Training Data Quality

The paper emphasizes the critical role of high-quality training data in developing robust dialogue capabilities. Data sets that offer limited responses, such as brief yes/no answers, can lead to reduced model performance by promoting terse responses. Thus, the researchers carefully curated their data sources, including portions of established datasets like LLaVA and Mini-GPT4, while selectively integrating samples from others like COCO Caption and OCR VQA.

Experimental Results and Observations

Experiments demonstrate that MultiModal-GPT effectively maintains continuous dialogues with users, showcasing its capability to handle long and context-driven interactions. The model can accurately identify objects within images, generate recipes, and provide restaurant recommendations based on visual cues—all indicating its strong numerical performance and multimodal understanding.

The model’s performance in OCR tasks and counting objects within images further highlights its versatility. By jointly training on language-only and vision-language data, the MultiModal-GPT exhibits a notable improvement in dialogue quality, suggesting that comprehensive and multimodal datasets contribute significantly to model efficacy.

Implications and Future Directions

The practical implications of MultiModal-GPT are substantial, with applications in developed AI dialogue systems capable of interacting through both visual and textual channels. Theoretically, it enriches findings related to multimodal learning frameworks by demonstrating effective data integration and model architecture modifications.

Future developments in AI could further refine such multimodal systems by exploring additional data sets or enhancing model architectures to address computational challenges. The versatility shown by MultiModal-GPT in handling diverse dialogue tasks could pave the way for more human-aligned AI assistants, offering seamless interaction across different users and applications.

In summary, MultiModal-GPT presents a noteworthy advancement in blending vision and language, structured to offer more human-like, efficient dialogue systems that communicate through multiple channels effectively.