Boosting Data Utilization for Multilingual Dense Retrieval | Paper Citation Record

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Boosting Data Utilization for Multilingual Dense Retrieval online" 'onlinestring :=

Reference 1

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Boosting Data Utilization for Multilingual Dense Retrieval write newline

Reference 2

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 3

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This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Boosting Data Utilization for Multilingual Dense Retrieval MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 4

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This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Boosting Data Utilization for Multilingual Dense Retrieval M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 5

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Boosting Data Utilization for Multilingual Dense Retrieval Unsupervised Cross-lingual Representation Learning at Scale

Reference 6

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Boosting Data Utilization for Multilingual Dense Retrieval BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Reference 8

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Reference 9

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This paper cites RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering.

Boosting Data Utilization for Multilingual Dense Retrieval RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 10

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This paper cites The Llama 3 Herd of Models.

Boosting Data Utilization for Multilingual Dense Retrieval The Llama 3 Herd of Models

Reference 11

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Reference 12

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This paper cites Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training.

Boosting Data Utilization for Multilingual Dense Retrieval Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training

Reference 13

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Observation 5256e750-7105-4dc8-9c80-0fcf322e05c1 · outbound

This paper cites Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval.

Boosting Data Utilization for Multilingual Dense Retrieval Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval

Reference 14

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This paper cites https://translate.google Google translate.

Boosting Data Utilization for Multilingual Dense Retrieval https://translate.google Google translate

Reference 15

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This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Boosting Data Utilization for Multilingual Dense Retrieval Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 16

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Reference 17

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Reference 18

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This paper cites A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers.

Boosting Data Utilization for Multilingual Dense Retrieval A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers

Reference 19

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 20

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Observation 50a6821e-2f83-475f-b886-fcf56886227c · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Boosting Data Utilization for Multilingual Dense Retrieval Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 21

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This paper cites FastText.zip: Compressing text classification models.

Boosting Data Utilization for Multilingual Dense Retrieval FastText.zip: Compressing text classification models

Reference 22

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This paper cites Bag of Tricks for Efficient Text Classification.

Boosting Data Utilization for Multilingual Dense Retrieval Bag of Tricks for Efficient Text Classification

Reference 23

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This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Boosting Data Utilization for Multilingual Dense Retrieval Dense Passage Retrieval for Open-Domain Question Answering

Reference 24

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Reference 25

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This paper cites Extending English IR methods to multi-lingual IR.

Boosting Data Utilization for Multilingual Dense Retrieval Extending English IR methods to multi-lingual IR

Reference 26

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Reference 27

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This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

Boosting Data Utilization for Multilingual Dense Retrieval NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 28

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This paper cites Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation.

Boosting Data Utilization for Multilingual Dense Retrieval Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation

Reference 29

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Boosting Data Utilization for Multilingual Dense Retrieval Conan-embedding: General Text Embedding with More and Better Negative Samples

Reference 30

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Reference 31

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This paper cites SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval.

Boosting Data Utilization for Multilingual Dense Retrieval SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval

Reference 32

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Reference 33

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Reference 34

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Reference 35

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Reference 36

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Reference 37

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Boosting Data Utilization for Multilingual Dense Retrieval Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models

Reference 38

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Reference 39

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Reference 40

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Reference 41

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Boosting Data Utilization for Multilingual Dense Retrieval History-Aware Conversational Dense Retrieval

Reference 43

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Boosting Data Utilization for Multilingual Dense Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 45

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Boosting Data Utilization for Multilingual Dense Retrieval M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

Reference 46

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Boosting Data Utilization for Multilingual Dense Retrieval Multi-Stage Document Ranking with BERT

Reference 49

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Boosting Data Utilization for Multilingual Dense Retrieval PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 51

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Boosting Data Utilization for Multilingual Dense Retrieval MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Reference 53

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Boosting Data Utilization for Multilingual Dense Retrieval Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

Reference 59

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Boosting Data Utilization for Multilingual Dense Retrieval Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 62

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Boosting Data Utilization for Multilingual Dense Retrieval HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 63

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Boosting Data Utilization for Multilingual Dense Retrieval C-Pack: Packed Resources For General Chinese Embeddings

Reference 64

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Observation 833d87c0-bdc6-4209-9c1a-e1ecea3b8608 · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Reference 65

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 66

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Unavailable: canonical work link unavailable.

Observation 15e697e9-54ee-48dc-b0de-29665c2f52bf · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Bruce Croft

Reference 67

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 68

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Unavailable: canonical work link unavailable.

Observation e3040a11-8ef8-4d71-b82a-d18157e0094d · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 69

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Observation 234eb505-213a-41b7-8a27-025dc3c1ff6f · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

Reference 70

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Observation eea9ae27-19ae-498f-999f-1076dbb8aae7 · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 71

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Observation 6d3264ae-cb63-4a13-9468-14fa2079d4f6 · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 72

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Observation a2d148ce-d8a1-4b4e-95b3-1718efec7c8f · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 73

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Observation bdf33954-ff3f-49ef-9b99-ec821d73cd5a · outbound

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Boosting Data Utilization for Multilingual Dense Retrieval Unresolved cited work

Reference 74

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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