arXiv:2410.14154v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. MLLMs involve significant external knowledge within their parameters; however, it is challenging to continually update these models with the latest knowledge, which involves huge computational costs and poor interpretability. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs. Considering the redundant information within vision modality, we first leverage the question to instruct the extraction of visual information through interactions with one set of learnable queries, minimizing irrelevant interference during retrieval and generation. Besides, we introduce a pre-trained multimodal adaptive fusion module to achieve question text-to-multimodal retrieval and integration of multimodal knowledge by projecting visual and language modalities into a unified semantic space. Furthermore, we present an Adaptive Selection Knowledge Generation (ASKG) strategy to train the generator to autonomously discern the relevance of retrieved knowledge, which realizes excellent denoising performance. Extensive experiments on open multimodal question-answering datasets demonstrate that RA-BLIP achieves significant performance and surpasses the state-of-the-art retrieval-augmented models.
Expert Commentary: The Future of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have been gaining considerable attention in recent years, and their potential as versatile models for vision-language tasks is becoming increasingly evident. However, one of the major challenges with these models is the constant update of external knowledge, as it involves significant computational costs and lacks interpretability. This is where retrieval augmentation techniques come into play, offering effective solutions for enhancing both LLMs and MLLMs.
In this study, a novel framework called multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP) is proposed. The framework takes advantage of the question to guide the extraction of visual information, minimizing irrelevant interference and allowing for more accurate retrieval and generation. Additionally, a pre-trained multimodal adaptive fusion module is introduced to achieve text-to-multimodal retrieval and integration of knowledge across different modalities.
One of the key features of the proposed framework is the Adaptive Selection Knowledge Generation (ASKG) strategy, which enables the generator to autonomously discern the relevance of retrieved knowledge. This strategy ensures excellent denoising performance and enhances the overall effectiveness of the model.
The results of extensive experiments conducted on multimodal question-answering datasets show that RA-BLIP outperforms existing retrieval-augmented models, demonstrating its potential as a state-of-the-art solution in the field.
Multi-disciplinary Nature and Relation to Multimedia Information Systems and AR/VR
The concepts explored in this study are highly multi-disciplinary and have strong connections to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
By combining language and vision modalities, multimodal large language models bridge the gap between textual and visual information, enabling more effective communication and understanding. This has direct implications for multimedia information systems, where the integration of various media types (such as text, images, videos, etc.) is crucial for efficient information retrieval and processing.
Furthermore, the use of retrieval augmentation techniques, as demonstrated in RA-BLIP, can significantly enhance the performance of multimedia information systems. By incorporating external knowledge and allowing for dynamic updates, these techniques enable better retrieval of relevant information and improve the overall user experience.
In the context of artificial reality, augmented reality, and virtual realities, multimodal large language models play a vital role in bridging the gap between virtual and real worlds. By understanding and generating both textual and visual content, these models can enable more immersive and interactive experiences in these virtual environments. This has implications for various applications, such as virtual reality gaming, education, and training simulations.
Overall, the findings of this study highlight the potential of multimodal large language models and retrieval augmentation techniques in advancing the field of multimedia information systems, as well as their relevance to the broader domains of artificial reality, augmented reality, and virtual realities.