Aussie AI

Reranker Optimizations

Last Updated 30 August, 2025

by David Spuler, Ph.D.

Research on Reranker Optimizations

Research papers include:

Vahe Aslanyan, June 11, 2024, Next-Gen Large Language Models: The Retrieval-Augmented Generation (RAG) Handbook, https://www.freecodecamp.org/news/retrieval-augmented-generation-rag-handbook/
Benjamin Clavié, 30 Aug 2024, rerankers: A Lightweight Python Library to Unify Ranking Methods, https://arxiv.org/abs/2408.17344 https://arxiv.org/pdf/2408.17344
Vivedha Elango, Sep 2024, Search in the age of AI- Retrieval methods for Beginners, https://ai.gopubby.com/search-in-the-age-of-ai-retrieval-methods-for-beginners-557621e12ded
Zhangchi Feng, Dongdong Kuang, Zhongyuan Wang, Zhijie Nie, Yaowei Zheng, Richong Zhang, 15 Oct 2024 (v2), EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations, https://arxiv.org/abs/2410.10315 https://github.com/BUAADreamer/EasyRAG
Rama Akkiraju, Anbang Xu, Deepak Bora, Tan Yu, Lu An, Vishal Seth, Aaditya Shukla, Pritam Gundecha, Hridhay Mehta, Ashwin Jha, Prithvi Raj, Abhinav Balasubramanian, Murali Maram, Guru Muthusamy, Shivakesh Reddy Annepally, Sidney Knowles, Min Du, Nick Burnett, Sean Javiya, Ashok Marannan, Mamta Kumari, Surbhi Jha, Ethan Dereszenski, Anupam Chakraborty, Subhash Ranjan, Amina Terfai, Anoop Surya, Tracey Mercer, Vinodh Kumar Thanigachalam, Tamar Bar, Sanjana Krishnan, Samy Kilaru, Jasmine Jaksic, Nave Algarici, Jacob Liberman, Joey Conway, Sonu Nayyar, Justin Boitano, 10 Jul 2024, FACTS About Building Retrieval Augmented Generation-based Chatbots, NVIDIA Research, https://arxiv.org/abs/2407.07858
Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
Y Huang, T Gao, J Zhang, X Liu, G Wang, 2024, Adapting Large Language Models for Biomedicine though Retrieval-Augmented Generation with Documents Scoring, 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, pages 5770-5775, DOI: 10.1109/BIBM62325.2024.10822725, https://www.computer.org/csdl/proceedings-article/bibm/2024/10822725/23oodpoidfq (Using an LLM-based reranker for medical research documents.)
MS Tamber, R Pradeep, J Lin, Jan 2025, LiT and Lean: Distilling Listwise Rerankers into Encoder-Decoder Models, https://cs.uwaterloo.ca/~jimmylin/publications/Tamber_Lin_ECIR2025.pdf
Bharani Subramaniam, 13 February 2025, Emerging Patterns in Building GenAI Products, https://martinfowler.com/articles/gen-ai-patterns/
Tanay Varshney, Annie Surla, Nave Algarici, Isabel Hulseman and Cherie Wang, Mar 06, 2025, How Using a Reranking Microservice Can Improve Accuracy and Costs of Information Retrieval, https://developer.nvidia.com/blog/how-using-a-reranking-microservice-can-improve-accuracy-and-costs-of-information-retrieval/
Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Flora D. Salim, Imran Razzak, 19 Mar 2025, Long Context Modeling with Ranked Memory-Augmented Retrieval, https://arxiv.org/abs/2503.14800
Jiashuo Sun, Xianrui Zhong, Sizhe Zhou, Jiawei Han, 16 May 2025 (v2), DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation https://arxiv.org/abs/2505.07233 https://github.com/GasolSun36/DynamicRAG
Chaitanya Sharma, 28 May 2025, Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers, https://arxiv.org/abs/2506.00054
Andrew Brown, Muhammad Roman, Barry Devereux, 8 Aug 2025, A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges, https://arxiv.org/abs/2508.06401
Latent Space, Aug 20, 2025, "RAG is Dead, Context Engineering is King" — with Jeff Huber of Chroma: What actually matters in vector databases in 2025, why “modern search for AI” is different, and how to ship systems that don’t rot as context grows, https://www.latent.space/p/chroma
Zekun Xu, Yudi Zhang, 22 Jul 2025, LLM-Enhanced Reranking for Complementary Product Recommendation, https://arxiv.org/abs/2507.16237
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang, 1 Jul 2024, Searching for Best Practices in Retrieval-Augmented Generation, https://arxiv.org/abs/2407.01219 Project: https://github.com/FudanDNN-NLP/RAG (Attempts to optimize the entire RAG system, including the various options for different RAG modules in the RAG pipeline, such as optimal methods for chunking, retrieval, embedding models, vector databases, prompt compression, reranking, repacking, summarizers, and other components.)
Atharva Nijasure, Tanya Chowdhury, James Allan, 10 Aug 2025, How Relevance Emerges: Interpreting LoRA Fine-Tuning in Reranking LLMs, https://arxiv.org/abs/2504.08780
Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme, 8 Aug 2025, Rank1: Test-Time Compute for Reranking in Information Retrieval, https://arxiv.org/abs/2502.18418
Bongsu Kim, 7 Aug 2025, RRRA: Resampling and Reranking through a Retriever Adapter, https://arxiv.org/abs/2508.11670
Haotian Chen, Qingqing Long, Meng Xiao, Xiao Luo, Wei Ju, Chengrui Wang, Xuezhi Wang, Yuanchun Zhou, Hengshu Zhu, 12 Aug 2025, SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs, https://arxiv.org/abs/2508.08742