Large Language Models as Foundations for Next-Gen Dense Retrieval: A
Comprehensive Empirical Assessment

Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment

22 August 2024

Zheng Liu

Shitao Xiao

Jun Zhao

Kang Liu

Papers citing "Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment"

6 / 6 papers shown

Title
LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models Hieu Man Nghia Trung Ngo Viet Dac Lai Ryan Rossi Franck Dernoncourt T. Nguyen 184 0 0 01 Jan 2025
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability Zhongxiang Sun Xiaoxue Zang Kai Zheng Yang Song Jun Xu Xiao Zhang Weijie Yu Yang Song Han Li 57 7 0 15 Oct 2024
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation Jianlv Chen Shitao Xiao Peitian Zhang Kun Luo Defu Lian Zheng Liu 115 333 0 05 Feb 2024
Making Large Language Models A Better Foundation For Dense Retrieval Chaofan Li Zheng Liu Shitao Xiao Yingxia Shao RALM 37 35 0 24 Dec 2023
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models Nandan Thakur Nils Reimers Andreas Rucklé Abhishek Srivastava Iryna Gurevych VLM 237 971 0 17 Apr 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 264 4,489 0 23 Jan 2020