MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2408.08521v2)

Abstract: Recent advancements in retrieval-augmented generation (RAG) have demonstrated impressive performance in the question-answering (QA) task. However, most previous works predominantly focus on text-based answers. While some studies address multimodal data, they still fall short in generating comprehensive multimodal answers, particularly for explaining concepts or providing step-by-step tutorials on how to accomplish specific goals. This capability is especially valuable for applications such as enterprise chatbots and settings such as customer service and educational systems, where the answers are sourced from multimodal data. In this paper, we introduce a simple and effective framework named MuRAR (Multimodal Retrieval and Answer Refinement). MuRAR enhances text-based answers by retrieving relevant multimodal data and refining the responses to create coherent multimodal answers. This framework can be easily extended to support multimodal answers in enterprise chatbots with minimal modifications. Human evaluation results indicate that multimodal answers generated by MuRAR are more useful and readable compared to plain text answers.

• The paper introduces MuRAR, a framework that integrates text generation, source-based multimodal retrieval, and answer refinement for comprehensive multimodal QA.
• It employs a modular design combining preliminary text answers with targeted multimodal data retrieval to enhance response coherence.
• Evaluations on 300 human-annotated queries reveal that MuRAR’s multimodal responses significantly improve readability, relevance, and user engagement.

MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering

The paper "MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering" by Zhu et al. addresses a critical gap in the domain of question answering (QA) systems, specifically in generating comprehensive multimodal responses. The authors develop MuRAR, a framework designed to enhance QA systems by incorporating multimedia elements such as images, videos, and tables, thereby improving the informativeness and user engagement of responses.

Introduction

The paper sets the stage by highlighting the limitations of current state-of-the-art QA systems that primarily produce text-based answers. While recent advancements in retrieval-augmented generation (RAG) techniques have significantly improved QA performance, they fall short in delivering multifaceted answers essential for complex queries. These limitations are especially pronounced in enterprise environments where understanding domain-specific topics often necessitates multimodal information sourced from extensive documentation, including images and videos.

Framework Overview

MuRAR aims to fill this gap by integrating multimodal data into generated answers. The framework comprises three core components: text answer generation, source-based multimodal retrieval, and multimodal answer refinement.

1. Text Answer Generation: Using an LLM fine-tuned for text snippet retrieval, the system first generates a preliminary text-based answer. This component employs a combination of retrieval techniques and LLM prompting to construct an initial draft of the answer based on a user's query.
2. Source-Based Multimodal Retrieval: This component enhances the initial text answer by retrieving relevant multimodal data. It involves two steps:
   • Source Attribution: Identifying and attributing specific parts of the text answer to the corresponding text sources.
   • Section-Level Multimodal Data Retrieval: Utilizing context-related text around the multimodal data (e.g., captions, transcript summaries) to ensure relevance and precision during retrieval.
3. Multimodal Answer Refinement: Finally, the framework integrates the retrieved multimodal data into the text answer, ensuring coherence and contextual relevance. The LLM is guided by prompts to combine the multimodal elements seamlessly with the textual content.

Evaluation and Results

The authors evaluated MuRAR on a human-annotated dataset comprising 300 questions. The results reveal that multimodal answers generated by MuRAR are significantly more useful, readable, and relevant than their text-only counterparts. The average scores across these metrics were higher for answers generated using both GPT-3.5 and GPT-4 models, demonstrating the system's efficacy in enhancing user understanding through multimodal information.

Implications and Future Work

The implications of this work are manifold. On a practical level, MuRAR can be readily integrated into enterprise-level AI assistants, thereby improving customer service, education systems, and other applications where multimodal content can augment understanding. Theoretically, this research underscores the potential of combining RAG techniques with multimodal data to address more complex QA tasks.

The authors identify future research directions, such as refining multimodal data retrieval processes and addressing the issue of redundancy in multimodal answers. Additionally, there is a potential for expanding the framework to handle even more complex query types and incorporating dynamic multimedia content.

Conclusion

MuRAR represents a significant step forward in the evolution of QA systems by effectively incorporating multimodal data to generate richer and more informative answers. This approach not only meets the demands of enterprise applications but also sets the foundation for future research aimed at enhancing AI-driven communication tools with advanced multimodal capabilities.