mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA (2411.15041v1)

Abstract: Advanced Multimodal LLMs (MLLMs) struggle with recent Knowledge-based VQA tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses. Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge, effectively expanding the knowledge scope. However, current mRAG methods have inherent drawbacks, including: 1) Performing retrieval even when external knowledge is not needed. 2) Lacking of identification of evidence that supports the query. 3) Increasing model complexity due to additional information filtering modules or rules. To address these shortcomings, we propose a novel generalized framework called \textbf{m}ultimodal \textbf{R}etrieval-\textbf{R}eflection-\textbf{A}ugmented \textbf{G}eneration (mR$^2$AG), which achieves adaptive retrieval and useful information localization to enable answers through two easy-to-implement reflection operations, preventing high model complexity. In mR$^2$AG, Retrieval-Reflection is designed to distinguish different user queries and avoids redundant retrieval calls, and Relevance-Reflection is introduced to guide the MLLM in locating beneficial evidence of the retrieved content and generating answers accordingly. In addition, mR$^2$AG can be integrated into any well-trained MLLM with efficient fine-tuning on the proposed mR$^2$AG Instruction-Tuning dataset (mR$^2$AG-IT). mR$^2$AG significantly outperforms state-of-the-art MLLMs (e.g., GPT-4v/o) and RAG-based MLLMs on INFOSEEK and Encyclopedic-VQA, while maintaining the exceptional capabilities of base MLLMs across a wide range of Visual-dependent tasks.

• The paper presents a novel mR²AG framework that incorporates dual reflection mechanisms to optimize external retrieval and enhance answer precision.
• It integrates an instruction-tuning dataset that adapts pre-trained MLLMs specifically for Knowledge-Based VQA tasks.
• Empirical results show over a 10% accuracy boost on INFOSEEK benchmarks, demonstrating improved efficiency and answer quality.

Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA

The paper "mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA" presents a new paradigm for leveraging Multimodal LLMs (MLLMs) in tackling Knowledge-based Visual Question Answering (VQA) tasks. These tasks demand more than the innate capabilities of traditional MLLMs due to the necessity of accessing up-to-date and comprehensive external knowledge. Typical applications such as INFOSEEK and Encyclopedic-VQA accentuate the deficiencies in frozen knowledge scopes, as evident from ambiguous responses generated by conventional models like GPT-4v/o.

Framework Overview

The authors advance the field through the innovative integration of a framework they term mR$^2$AG (multimodal Retrieval-Reflection-Augmented Generation). The goal is to transcend the limitations of current multimodal Retrieval-Augmented Generation (mRAG) methods typified by unnecessary retrieval and complex information processing overheads that escalate model complexity. mR$^2$AG progresses this by incorporating two pivotal reflection operations within a generalized framework, explicitly delineating the retrieval adaptation and the localization of critical information needed to generate precise answers.

Key Contributions

1. Reflection Mechanisms:
   • Retrieval-Reflection: Enhances the decision-making process regarding the necessity of invoking external retrievals. This step precludes needless retrieval actions, significantly preserving model efficiency.
   • Relevance-Reflection: Facilitates the model's ability to pinpoint beneficial evidence within retrieved content, positioning it to generate answers based on this refined focus.
2. Instruction-Tuning Dataset:
   • The proposed mR$^2$AG framework seamlessly integrates with pre-trained MLLMs facilitated by a newly introduced Instruction-Tuning dataset (mR$^2$AG-IT). This dataset is expertly crafted to adapt MLLMs specifically for Knowledge-Based VQA tasks.

Experimental Insights

Upon empirical evaluation, the mR$^2$AG framework markedly surpasses state-of-the-art MLLMs on the INFOSEEK and Encyclopedic-VQA benchmarks, delineating its efficiency in outclassing naive mRAG models. These performance gains are observed across both single-hop and more complex multi-answer question settings.

When measured against conventional methods without external knowledge bases, mR$^2$AG, especially when bolstered by Wikipedia as an auxiliary knowledge resource, consistently achieves higher accuracy rates—demonstrated with an increase of over 10\% on INFOSEEK test sets compared to prior best-performing models.

Practical and Theoretical Implications

The paper illustrates the robustness of mR$^2$AG in refining MLLMs’ capabilities across both Visual-dependent and Knowledge-based VQA tasks. By establishing a structured mechanism for determining when retrieval is advantageous and assessing the prudence of the sourced evidence, the model maintains its adeptness in visual tasks while substantively heightening its comprehension and response accuracy in knowledge-centric scenarios.

Future Outlook

With its strategic resource allocation and evidence assessment aligned with the generative modeling capabilities of MLLMs, future developments may delve into optimizing retrieval schemas further and exploring graph-based data augmentation to enrich the knowledge framework. Additional fine-tuning and iterative model training could cement the framework’s prowess across an expanded collection of dynamic multimodal data applications.

In conclusion, this paper envisions a scalable, efficient, and insightful architecture for multimodal VQA, catering to the escalating demands of modern AI applications, particularly those hinged on extensive and updated knowledge integration. The proposed mR$^2$AG framework paves the way for a more nuanced understanding and processing of complex queries across diverse knowledge domains.