Unified Multimodal Interleaved Document Representation for Retrieval (2410.02729v2)

Abstract: Information Retrieval (IR) methods aim to identify documents relevant to a query, which have been widely applied in various natural language tasks. However, existing approaches typically consider only the textual content within documents, overlooking the fact that documents can contain multiple modalities, including images and tables. Also, they often segment each long document into multiple discrete passages for embedding, which prevents them from capturing the overall document context and interactions between paragraphs. To address these two challenges, we propose a method that holistically embeds documents interleaved with multiple modalities by leveraging the capability of recent vision-LLMs that enable the processing and integration of text, images, and tables into a unified format and representation. Moreover, to mitigate the information loss from segmenting documents into passages, instead of representing and retrieving passages individually, we further merge the representations of segmented passages into one single document representation, while we additionally introduce a reranking strategy to decouple and identify the relevant passage within the document if necessary. Then, through extensive experiments on diverse IR scenarios considering both the textual and multimodal queries, we show that our approach substantially outperforms relevant baselines, thanks to the consideration of the multimodal information within documents.

• The paper introduces a unified multimodal interleaved representation that integrates text, images, and tables to address retrieval limitations.
• It leverages advanced vision-language models and a novel reranking strategy to preserve contextual coherence and enhance fine-grained retrieval accuracy.
• Experimental results demonstrate significant improvements in metrics like Recall@K and MRR@K across multiple benchmark datasets.

Unified Multi-Modal Interleaved Document Representation for Information Retrieval

The paper "Unified Multi-Modal Interleaved Document Representation for Information Retrieval" addresses critical limitations in current Information Retrieval (IR) systems, focusing on the integration of multi-modal content within documents. Traditional IR methodologies primarily rely on textual data, often neglecting the diverse modalities present in modern documents, such as images and tables. Moreover, these methods frequently segment long documents into discrete passages, which can hinder the capture of overall document context, resulting in suboptimal retrieval performance.

Methodological Insights

The authors propose a comprehensive approach that leverages recent advancements in Vision-LLMs (VLMs) to holistically represent documents interleaved with various modalities. This method allows for the integration of text, images, and tables into a unified document representation, preserving contextual and relational information across the entire document. The approach departs from prior methods by avoiding fragmented visual representations and enhancing the accuracy of document retrieval through a unified token sequence processed via VLMs.

The paper also introduces an innovative reranking strategy that helps in identifying the most pertinent sections within a document. This mechanism effectively bridges the gap between coarse-grained document-level retrieval and fine-grained passage-level retrieval, enabling the system to pin down the relevant sections without segmenting the document indiscriminately.

Core Contributions

1. Unified Multi-Modal Representation: The research emphasizes the importance of incorporating multimodal content for document representation, using VLMs to handle complex interleavings of text and images.
2. Enhanced Retrieval Strategy: By merging representations of segmented passages into a single document representation, the proposed method retains the structural coherence of documents, which significantly boosts retrieval performance.
3. Effective Reranking Mechanism: A reranking system identifies the essential sections within a document, complementing the document-level retrieval with enhanced passage-level accuracy.

Experimental Evaluation

The paper reports extensive experimental results across several benchmark datasets, including Encyclopedic-VQA and ViQuAE. The proposed method consistently outperformed existing baselines, showcasing substantial improvements, especially in scenarios involving multimodal queries. The experiments highlight that incorporating diverse modalities within document representations provides more effective retrieval outcomes, with the interleaved approach yielding notable gains in metrics like Recall@K and Mean Reciprocal Rank (MRR@K).

Interestingly, the research also explores document and section retrieval for tables, pointing out that even though such tasks are challenging due to the similarity of tables within documents, the interleaved format provides competitive performance advantages when properly fine-tuned on target datasets.

Implications and Future Research

The implications of this research are significant for both the theoretical understanding and practical application of IR systems. By expanding the scope of IR to include interleaved multimodal document representations, the research paves the way for more nuanced and effective retrieval systems capable of handling the complexity of modern information sources. This approach could have substantial impacts on applications like search engines and knowledge base systems.

Future research could explore the optimization of computational resources for processing interleaved documents in long contexts and developing more sophisticated reranking models that can fully leverage document context. Exploring these avenues may enhance the retrieval capabilities of IR systems further and adapt them to the growing demands of multimodal data integration.