GeAR: Generation Augmented Retrieval (2501.02772v2)

Abstract: Document retrieval techniques are essential for developing large-scale information systems. The common approach involves using a bi-encoder to compute the semantic similarity between a query and documents. However, the scalar similarity often fail to reflect enough information, hindering the interpretation of retrieval results. In addition, this process primarily focuses on global semantics, overlooking the finer-grained semantic relationships between the query and the document's content. In this paper, we introduce a novel method, $\textbf{Ge}$neration $\textbf{A}$ugmented $\textbf{R}$etrieval ($\textbf{GeAR}$), which not only improves the global document-query similarity through contrastive learning, but also integrates well-designed fusion and decoding modules. This enables GeAR to generate relevant context within the documents based on a given query, facilitating learning to retrieve local fine-grained information. Furthermore, when used as a retriever, GeAR does not incur any additional computational cost over bi-encoders. GeAR exhibits competitive retrieval performance across diverse scenarios and tasks. Moreover, qualitative analysis and the results generated by GeAR provide novel insights into the interpretation of retrieval results. The code, data, and models will be released at \href{https://github.com/microsoft/LMOps}{https://github.com/microsoft/LMOps}.

• The paper introduces a novel framework that integrates retrieval with generation using fused representations to capture fine-grained semantic nuances.
• It employs a systematic data synthesis pipeline with large language models to overcome data scarcity while preserving computational efficiency.
• Experimental results demonstrate robust performance in question-answer and relevant information retrieval, offering new insights for advanced AI search applications.

Insights into Generation Augmented Retrieval (GeAR)

The paper "Generation Augmented Retrieval (GeAR)" introduces a novel methodology in the domain of document retrieval aiming to enhance the retrieval process by incorporating generation capabilities. Traditional methodologies heavily rely on bi-encoder models that assess semantic similarity between query and document as scalar data, which often results in an insufficient representation of the complex relationships implicit in large documents. This shortcoming hampers the interpretability of retrieval outputs and fails to capture fine-grained semantic nuances, a gap that GeAR seeks to address.

Methodology and Contributions

GeAR proposes an innovative framework that seamlessly integrates retrieval with generation through the application of well-devised fusion and decoding modules. Benefiting from these advancements, GeAR generates relevant content from existing documents by transforming the query and document into a fused representation, thereby enriching the retrieval process with fine-grained information localization. Despite enhancing capability, GeAR maintains computational efficiency equivalent to existing bi-encoder methods.

The paper outlines a systematic data synthesis pipeline utilizing LLMs to prepare high-quality training data, critical in alleviating data scarcity issues commonly encountered in this research area. Noteworthily, this process demonstrates that GeAR achieves robust performance across varied datasets and tasks without exerting a heavier computational load compared to its bi-encoder counterparts.

Results and Analysis

In experimental evaluations, GeAR shows competitive performance in retrieval and localization tasks across multiple datasets. Notably, it excels in question-answer retrieval (QAR) and relevant information retrieval (RIR), demonstrating its adaptability and effectiveness in generating specific document content responsive to user queries.

GeAR differentiates itself by not only locating documents and fine-grained units within those documents but also by generating auxiliary information that aids in understanding retrieval outcomes. This capability is particularly highlighted in its qualitative analysis, where GeAR's result generation provides new insights into the interpretation of retrieval results, offering a refreshing perspective that could influence future retrieval and generation tasks.

Implications and Future Directions

The theoretical implications of GeAR extend to the emerging fusion of natural language understanding and generation disciplines. The integration of these processes within an AI framework signifies a step toward more nuanced and coherent machine comprehension and response generation. Practically, GeAR's methodology presents a valuable asset for applications in web search and information retrieval tasks demanding high precision, like open-domain question answering and retrieval-augmented generation (RAG).

Future developments could focus on optimizing the balance between retrieval and generation tasks, refining the synergy across these computational processes without compromising efficiency. Additionally, exploring the expansion of context lengths beyond current limitations could further advance GeAR's applicability in handling long-form document retrievals effectively.

In summation, GeAR exemplifies an evolutionary step in retrieval systems, underscoring the benefit of integrating generation functionalities to achieve more refined and interpretable retrieval outcomes. This advancement marks a noteworthy progression towards more sophisticated and integrated NLP systems, enhancing both the theoretical discourse and practical applications within the field of AI.