Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment
This work addresses retrieval challenges in RAG systems for applications requiring real-time, resource-efficient cross-modal text alignment, such as between programming code and pseudocode or different languages, though it appears incremental as it builds on adapter modules and projection techniques.
The paper tackles the problem of semantic gaps in cross-modal text retrieval for Retrieval-Augmented Generation systems, introducing a projection-based method that significantly outperforms traditional retrieval methods like Okapi BM25 and Dense Passage Retrieval while approaching the accuracy of Sentence Transformers.
Retrieval-Augmented Generation (RAG) systems enhance text generation by incorporating external knowledge but often struggle when retrieving context across different text modalities due to semantic gaps. We introduce a generalized projection-based method, inspired by adapter modules in transfer learning, that efficiently bridges these gaps between various text types, such as programming code and pseudocode, or English and French sentences. Our approach emphasizes speed, accuracy, and data efficiency, requiring minimal resources for training and inference. By aligning embeddings from heterogeneous text modalities into a unified space through a lightweight projection network, our model significantly outperforms traditional retrieval methods like the Okapi BM25 algorithm and models like Dense Passage Retrieval (DPR), while approaching the accuracy of Sentence Transformers. Extensive evaluations demonstrate the effectiveness and generalizability of our method across different tasks, highlighting its potential for real-time, resource-constrained applications.