Enhancing hybrid information retrieval with hyperdimensional computing: a framework for efficient and robust knowledge-intensive NLP
Abstract
Retrieval-Augmented Generation (RAG) has emerged as a practical framework for augmenting large language models (LLMs) with external knowledge sources. However, standard RAG pipelines often remain computationally intensive and exhibit sensitivity to input noise, particularly in real-world scenarios where queries can be ambiguous, noisy, or semantically varied. This thesis proposes HD-RAG, a hybrid retrieval system that integrates Hyperdimensional Computing (HDC) as a symbolic filtering layer prior to dense reranking within the RAG pipeline, with the aim of improving retrieval robustness and computational efficiency while maintaining semantic relevance.The HD-RAG framework encodes each document as both a high-dimensional binary vector—via a learnable projection from SBERT embeddings—and a dense vector for downstream reranking. Retrieval proceeds in two stages: symbolic filtering using Hamming distance to rapidly select candidate passages, followed by dense reranking based on cosine similarity. Experiments on a 100,000-passage subset of the MS MARCO ranking dataset show that HD-RAG achieves performance comparable to strong SBERT-only baselines on clean queries, and demonstrates improved robustness under some types of input noise, particularly synonym and adversarial perturbations. For other noise types, such as character-level typos or combined noise, the hybrid approach offers only marginal gains or, in some cases, slight degradations.Fusion of symbolic and dense scores was…
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