Cos-mix: Cosine Similarity And Distance Fusion For Improved Information Retrieval

Abstract

This study proposes a novel hybrid retrieval strategy for Retrieval-Augmented Generation (RAG) that integrates cosine similarity and cosine distance measures to improve retrieval performance, particularly for sparse data. The traditional cosine similarity measure is widely used to capture the similarity between vectors in high-dimensional spaces. However, it has been shown that this measure can yield arbitrary results in certain scenarios. To address this limitation, we incorporate cosine distance measures to provide a complementary perspective by quantifying the dissimilarity between vectors. Our approach is experimented on proprietary data, unlike recent publications that have used open-source datasets. The proposed method demonstrates enhanced retrieval performance and provides a more comprehensive understanding of the semantic relationships between documents or items. This hybrid strategy offers a promising solution for efficiently and accurately retrieving relevant information in

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyjuvekar2024cos

Related papers

• Advancing Similarity Search With Genai: A Retrieval Augmented Generation Approach (2024)0.00
• Gear: Generation Augmented Retrieval (2025)2.79
• A Fast Text Similarity Measure For Large Document Collections Using Multi-reference Cosine And Genetic Algorithm (2018)4.52
• CART: A Generative Cross-modal Retrieval Framework With Coarse-to-fine Semantic Modeling (2024)3.58
• Re-ranking The Context For Multimodal Retrieval Augmented Generation (2025)0.00
• Retro-li: Small-scale Retrieval Augmented Generation Supporting Noisy Similarity Searches And Domain Shift Generalization (2024)4.06
• Cross-modal RAG: Sub-dimensional Text-to-image Retrieval-augmented Generation (2025)0.00
• Mor: Better Handling Diverse Queries With A Mixture Of Sparse, Dense, And Human Retrievers (2025)2.26