ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2208.05753
  4. Cited By
Disentangled Modeling of Domain and Relevance for Adaptable Dense
  Retrieval

Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

11 August 2022
Jingtao Zhan
Qingyao Ai
Yiqun Liu
Jiaxin Mao
Xiaohui Xie
M. Zhang
Shaoping Ma
ArXivPDFHTML

Papers citing "Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval"

4 / 4 papers shown
Title
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot
  Classification
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
Yongxin Huang
Kexin Wang
Sourav Dutta
Raj Nath Patel
Goran Glavas
Iryna Gurevych
VLM
13
4
0
01 Nov 2023
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage
  Retrieval
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Luyu Gao
Jamie Callan
RALM
152
329
0
12 Aug 2021
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information
  Retrieval Models
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Nandan Thakur
Nils Reimers
Andreas Rucklé
Abhishek Srivastava
Iryna Gurevych
VLM
229
964
0
17 Apr 2021
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for
  Open-Domain Question Answering
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
Yingqi Qu
Yuchen Ding
Jing Liu
Kai Liu
Ruiyang Ren
Xin Zhao
Daxiang Dong
Hua-Hong Wu
Haifeng Wang
RALM
OffRL
209
593
0
16 Oct 2020
1