Introduction

In the ever-evolving field of artificial intelligence, multimodal retrieval augmented generation (MM-RAG) is an emerging technique that bridges the gap between understanding and generating content across diverse data types. It combines the strengths of Large Language Models (LLMs) and retrieval systems to generate a response based on a prompt or question.

How MM-RAG Works?

MM-RAG leverages a two-step process:

1. Retrieval Stage: A retrieval system finds relevant information from a vast knowledge base in response to a prompt. This information can include text documents, images, or other modalities.
2. Generation Stage: An LLM is fed the prompt along with the retrieved information. The LLM then uses this information to generate a more comprehensive and informative response.

What are the benefits of MM-RAG?

MM-RAG offers several advantages over traditional LLM-based approaches:

• Improved Accuracy: By grounding responses in factual information retrieved from external sources, MM-RAG reduces the likelihood of hallucinations.
• Enhanced Scalability: The knowledge base used by the retrieval system can be constantly updated, allowing the MM-RAG to stay current without requiring continuous LLM training.
• Better Generalisability: MM-RAG can handle a wide range of prompts and questions due to its ability to access and process information from various modalities.

What are the applications of MM-RAG?

MM-RAG has the potential to revolutionize various fields:

• Question and Answering Systems: MM-RAG can be used to create intelligent question-answering systems that can access and process information from diverse sources to provide comprehensive answers.
• Document Summarization: MM-RAG can generate more informative and accurate summaries of factual documents by incorporating retrieved information.
• Chatbots and Virtual Assistants: MM-RAG can enhance chatbots and virtual assistants by enabling them to provide more helpful and informative responses based on retrieved knowledge.

What are the challenges of Multimodal RAG?

Multimodal Retrieval: Enterprise data consists of many different pieces: text, images, charts, graphs, and more. Consider a folder containing high-resolution images alongside PDFs containing a mix of textual information, tables, diagrams, and some audio files. Each modality presents its unique challenges, and managing information across these modalities becomes crucial.

LLM Integration: Integrating the retrieved information seamlessly into the LLM’s prompt requires careful consideration to ensure the LLM can effectively leverage the retrieved knowledge.

Knowledge Base Construction and Maintenance: Building and maintaining a comprehensive and up-to-date knowledge base is crucial for MM-RAG’s success.

What are the challenges of Individual Modalities?

Images: Consider an image. While some images convey general visual information, others—such as charts and diagrams—contain intricate details and context. A robust multimodal framework must capture these nuances effectively.

Text: Textual content can be dense, informative, or explanatory. Aligning the semantic representation of text with that of associated images or charts is essential for coherent communication.

What are the approaches to Multimodal Retrieval Augmented Generation?

To address these challenges, researchers have explored several Multimodal Retrieval approaches:

• Embedding All Modalities into the Same Vector Space: In the case of images and text, models like CLIP encode both modalities into a shared vector space. This allows seamless interchangeability between text-only and multimodal retrieval augmented generation.
• During the generation phase, we replace the large language model (LLM) with a multimodal LLM (MLLM) to handle questions and answers.
• Grounding Modalities into One Primary Modality: We create a unified representation by aligning all modalities to a primary one (e.g., text). This simplifies the retrieval process and ensures consistency across different data types.
• Separate Stores for Different Modalities: Maintain separate repositories for each modality (e.g., one for images and another for text). This approach allows targeted retrieval and generation based on the specific context.

MM-RAG in Practice

MM-RAG combines the power of language models (such as GPT-4) with multimodal retrievers. The model retrieves relevant examples from images, audio, and text during text generation to inform its completions. This fusion of information retrieval and text generation opens exciting possibilities for creating context-aware, informative, and engaging content.

Conclusion

In summary, MM-RAG represents a leap forward in AI capabilities, enabling systems to understand and generate responses across diverse data types. It is a rapidly evolving field with the potential to significantly improve the capabilities of LLMs.
As research in multimodal retrieval and LLM integration progresses, MM-RAG is poised to become a cornerstone of various NLP applications