Feb. 27, 2024, 5:44 a.m. | Ziyi Chen, Jize Jiang, Daqian Zuo, Heyi Tao, Jun Yang, Yuxiang Wei

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.12430v3 Announce Type: replace-cross
Abstract: In recent RAG approaches, rerankers play a pivotal role in refining retrieval accuracy with the ability of revealing logical relations for each pair of query and text. However, existing rerankers are required to repeatedly encode the query and a large number of long retrieved text. This results in high computational costs and limits the number of retrieved text, hindering accuracy. As a remedy of the problem, we introduce the Efficient Title Reranker via Broadcasting Query …

abstract accuracy arxiv cs.ai cs.cl cs.ir cs.lg encode knowledge nlp pivotal query rag relations retrieval role text type

Data Scientist (m/f/x/d)

@ Symanto Research GmbH & Co. KG | Spain, Germany

Aumni - Site Reliability Engineer III - MLOPS

@ JPMorgan Chase & Co. | Salt Lake City, UT, United States

Senior Data Analyst

@ Teya | Budapest, Hungary

Technical Analyst (Data Analytics)

@ Contact Government Services | Chicago, IL

Engineer, AI/Machine Learning

@ Masimo | Irvine, CA, United States

Private Bank - Executive Director: Data Science and Client / Business Intelligence

@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India