Enhancing Multimodal Large Language Models with Hierarchical Retrieval of External Knowledge

Core Concepts

Integrating an external knowledge base of multimodal documents into a multimodal large language model through a hierarchical retrieval pipeline to improve its ability to answer questions that require external knowledge.

Abstract

The paper proposes a novel approach, termed Wiki-LLaVA, that augments a multimodal large language model (MLLM) with the capability to retrieve and leverage external knowledge from a knowledge base of multimodal documents.
The key highlights are:

The authors identify limitations of existing MLLMs in answering questions that require external knowledge beyond what is contained in the model's training data.
To address this, they introduce a hierarchical retrieval pipeline that first retrieves the most relevant document from an external knowledge base using the input image as a query, and then identifies the most relevant passages within that document to provide as additional context to the MLLM.
The retrieved passages are then integrated into the input of the MLLM, which is fine-tuned to effectively utilize this external knowledge in generating answers.
Extensive experiments on the Encyclopedic-VQA and InfoSeek datasets demonstrate the effectiveness of the proposed approach, with significant improvements over standard MLLM baselines that do not leverage external knowledge.
The authors also analyze the importance of the fine-tuning datasets and the preservation of the MLLM's performance on other evaluation benchmarks.
Overall, the work presents a novel and effective approach to enhance the capabilities of MLLMs by integrating external multimodal knowledge, paving the way for future research in this direction.

Stats

The dataset Encyclopedic-VQA contains around 221k question-answer pairs associated with 16.7k different fine-grained entities, with up to 5 images representing the same entity. The dataset also comes with a knowledge base composed of 2M Wikipedia articles.
The InfoSeek dataset contains 1.3M image-question-answer triplets corresponding to around 11k different entities, with images derived from the OVEN dataset. A knowledge base of 6M Wikipedia entities is provided along with the dataset.

Quotes

"Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality."
"Our approach, termed Wiki-LLaVA, aims at integrating an external knowledge source of multimodal documents, which is accessed through a hierarchical retrieval pipeline."
"Relevant passages, using this approach, are retrieved from the external knowledge source and employed as additional context for the LLM, augmenting the effectiveness and precision of generated dialogues."

Key Insights Distilled From

Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs

by Davide Caffa... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15406.pdf

Deeper Inquiries

How can the hierarchical retrieval pipeline be further improved to better identify the most relevant passages from the external knowledge base?

To enhance the hierarchical retrieval pipeline for better identification of relevant passages, several improvements can be considered:

Fine-tuning Retrieval Models: Fine-tuning the retrieval models used in the pipeline with domain-specific data can help improve the accuracy of passage retrieval. By training the models on a dataset that closely resembles the target domain, the retrieval process can be optimized for better performance.

Semantic Matching: Incorporating semantic matching techniques can help in identifying passages that are semantically similar to the query. Utilizing methods like semantic embeddings or contextualized representations can improve the matching process and retrieve more relevant information.

Multi-stage Retrieval: Implementing a multi-stage retrieval process where the retrieved documents are further filtered in subsequent stages can help in narrowing down to the most relevant passages. Each stage can focus on different aspects of relevance, such as topical relevance, specificity, or context matching.

Cross-modal Retrieval: Integrating cross-modal retrieval techniques that consider both textual and visual information can be beneficial, especially in scenarios where the query involves both modalities. By aligning textual and visual features, the pipeline can retrieve more comprehensive and relevant passages.

Feedback Mechanisms: Implementing feedback mechanisms where the system learns from user interactions and adjusts the retrieval process based on feedback can lead to continuous improvement. User feedback on the relevance of retrieved passages can be used to refine the retrieval models over time.

Knowledge Graph Integration: Incorporating knowledge graphs to represent relationships between entities and concepts in the external knowledge base can aid in better understanding the context of the query. By leveraging the structured information in knowledge graphs, the retrieval pipeline can retrieve more contextually relevant passages.

How can the proposed framework be extended to incorporate other types of external knowledge sources beyond just textual documents, such as structured knowledge bases or visual knowledge?

The proposed framework can be extended to incorporate diverse external knowledge sources by adapting the retrieval and integration mechanisms to accommodate different types of data:

Structured Knowledge Bases: For structured knowledge bases, the framework can utilize entity linking techniques to map entities mentioned in the query to entities in the knowledge base. By leveraging the structured nature of the data, the system can retrieve specific facts or relationships related to the entities mentioned in the query.

Visual Knowledge: To incorporate visual knowledge, the framework can integrate computer vision models to extract information from images or videos. Visual features extracted from the input can be used to retrieve relevant visual content from external sources, which can then be integrated into the context for answering queries that involve visual information.

Knowledge Graph Integration: Extending the framework to incorporate knowledge graphs can provide a rich source of interconnected information. By integrating knowledge graph traversal techniques, the system can navigate through the graph to retrieve relevant information based on the query context and relationships between entities.

Multi-modal Fusion: To handle multi-modal external knowledge sources, the framework can employ multi-modal fusion techniques to combine information from different modalities. By integrating textual, visual, and structured data sources, the system can provide more comprehensive and contextually rich responses to queries that require multi-modal information.

Domain-specific Extensions: Depending on the application domain, the framework can be customized to incorporate domain-specific external knowledge sources. This customization may involve developing specialized retrieval mechanisms and integration strategies tailored to the unique characteristics of the external data sources in that domain.

By adapting the framework to accommodate a variety of external knowledge sources, the system can provide more diverse and comprehensive responses to queries that require information beyond textual documents.