# COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval

arXiv: https://arxiv.org/abs/2406.00638 · HTML: https://mlsanity.com/paper/2406.00638/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://mlsanity.com/paper/2405.14831/) (2024)
- [QuALITY: Question Answering with Long Input Texts, Yes!](https://mlsanity.com/paper/2112.08608/) (2021)
- [Corrective Retrieval Augmented Generation](https://mlsanity.com/paper/2401.15884/) (2024)
- [Is Cosine-Similarity of Embeddings Really About Similarity?](https://mlsanity.com/paper/2403.05440/) (2024)
- [Information Retrieval: Recent Advances and Beyond](https://mlsanity.com/paper/2301.08801/) (2023)
- [Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation](https://mlsanity.com/paper/2405.13622/) (2024)
- [Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface](https://mlsanity.com/paper/2310.04205/) (2023)

## Built on by (most cited first, 4 of 4)

- [Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability](https://mlsanity.com/paper/2406.11424/) (2024)
- [Introducing a new hyper-parameter for RAG: Context Window Utilization](https://mlsanity.com/paper/2407.19794/) (2024)
- [A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning](https://mlsanity.com/paper/2408.05141/) (2024)
- [KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversations](https://mlsanity.com/paper/2504.04569/) (2025)

## Abstract (arXiv)

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 knowledge-intensive applications, leveraging techniques such as BM25 (sparse) retrieval , vector (Dense) retrieval, and cosine distance based retrieval to facilitate efficient information retrieval.