QZhou-Embedding Technical Report | Paper Citation Record

Observation ba1c6f50-39a9-44d2-ba30-e42f1a6b9ed7 · outbound

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Reference 1

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Observation 514bf2f8-483b-404b-bf60-f3e6c30b9aa1 · outbound

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

QZhou-Embedding Technical Report BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

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Observation 9f5a16d0-1099-4c87-a044-e41a9c64180a · outbound

This paper cites Exploring the limits of tr ansfer learn- ing with a uniﬁed text-to-text transformer.

QZhou-Embedding Technical Report Exploring the limits of tr ansfer learn- ing with a uniﬁed text-to-text transformer

Reference 3

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Observation 1d31a5dd-ad3f-441d-99f3-58b474c13336 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

QZhou-Embedding Technical Report Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 4

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Observation 02ba9c99-4549-4eb3-af84-d2076a166722 · outbound

This paper cites Unsupervised dense information retrieval with contrastive learning.

QZhou-Embedding Technical Report Unsupervised dense information retrieval with contrastive learning

Reference 5

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Observation faf19577-814c-470e-9cab-cd38d4fdd175 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

QZhou-Embedding Technical Report Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 6

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Reference 7

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Observation 5c533a42-6bc2-4ee7-8bcf-8db07f7f83f2 · outbound

This paper cites Large Dual Encoders Are Generalizable Retrievers.

QZhou-Embedding Technical Report Large Dual Encoders Are Generalizable Retrievers

Reference 8

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Observation 3df3abd5-daa9-4b3b-9cdc-334b2c0f8d6d · outbound

This paper cites Kaplan, Pra- fulla Dhariwal, Arvind Neelakantan et al.

QZhou-Embedding Technical Report Kaplan, Pra- fulla Dhariwal, Arvind Neelakantan et al

Reference 9

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Observation 63516512-bef2-402f-b93e-6eba64dd93c3 · outbound

This paper cites ”F ine-tuning llama for multi-stage text retrieval.” In Proceedings of the 47th Int ernational ACM SIGIR Conference on Research and Development in Information Re trieval, pp.

QZhou-Embedding Technical Report ”F ine-tuning llama for multi-stage text retrieval.” In Proceedings of the 47th Int ernational ACM SIGIR Conference on Research and Development in Information Re trieval, pp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.914136Z

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Observation 4c0647da-fb15-444b-ad50-4730438d39a0 · outbound

This paper cites Repetition Improves Language Model Embeddings.

QZhou-Embedding Technical Report Repetition Improves Language Model Embeddings

Reference 11

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Observation 8fcd503f-8a28-4ff7-bb11-8a00e6c43e0d · outbound

This paper cites ”Llm2vec: Large languag e models are secretly powerful text encoders.” arXiv preprint arXiv:2404.0596 1 (2024).

QZhou-Embedding Technical Report ”Llm2vec: Large languag e models are secretly powerful text encoders.” arXiv preprint arXiv:2404.0596 1 (2024)

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-05T14:11:10.398078Z

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Observation b13fe0b4-d50d-429f-9d2c-3997ced18d95 · outbound

This paper cites an unresolved cited work.

QZhou-Embedding Technical Report Unresolved cited work

Reference 13

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Observation 58f9386d-24d1-417d-af5a-3672af31852d · outbound

This paper cites Jasper and Stella: distillation of SOTA embedding models.

QZhou-Embedding Technical Report Jasper and Stella: distillation of SOTA embedding models

Reference 14

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Observation c3a5225e-a781-4890-b7d1-3617bc8cb863 · outbound

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

QZhou-Embedding Technical Report M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 15

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Observation 9106e9d1-f2de-4def-83cb-b50439a10acc · outbound

This paper cites ”Learning more eﬀective repre senta- tions for dense retrieval through deliberate thinking before sear ch.” arXiv preprint arXiv:2502.12974 (2025).

QZhou-Embedding Technical Report ”Learning more eﬀective repre senta- tions for dense retrieval through deliberate thinking before sear ch.” arXiv preprint arXiv:2502.12974 (2025)

Reference 16

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Observation 6df8c9ff-03f9-40b0-9cf4-3fee203dd0e9 · outbound

This paper cites LGAI-EMBEDDING-Preview Technical Report.

QZhou-Embedding Technical Report LGAI-EMBEDDING-Preview Technical Report

Reference 17

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Observation 5e70ff41-5e9e-4542-94a8-37d96d36f0fc · outbound

This paper cites ”Nv-embed: Improved t echniques for training llms as generalist embedding models.” arXiv preprint arXiv:2405 .17428 (2024).

QZhou-Embedding Technical Report ”Nv-embed: Improved t echniques for training llms as generalist embedding models.” arXiv preprint arXiv:2405 .17428 (2024)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.895208Z

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Observation 8eac023e-73a7-403c-b818-e369d1e70926 · outbound

This paper cites ”Qwen2 technical report.” arXiv preprint arXiv:24 07.10671 (2024).

QZhou-Embedding Technical Report ”Qwen2 technical report.” arXiv preprint arXiv:24 07.10671 (2024)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.886335Z

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Observation a034f753-4438-4ad0-8365-8beafb4ba744 · outbound

This paper cites ”C-pack: Packed resources for general chinese embedd ings.” In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp.

QZhou-Embedding Technical Report ”C-pack: Packed resources for general chinese embedd ings.” In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.878096Z

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Observation e8882707-aa02-47b7-ae50-f8a1b01b9515 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

QZhou-Embedding Technical Report MTEB: Massive Text Embedding Benchmark

Reference 23

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Observation 7cc158bc-2a9a-48fa-b797-c6f8ff80140f · outbound

This paper cites Conan-embedding: General Text Embedding with More and Better Negative Samples.

QZhou-Embedding Technical Report Conan-embedding: General Text Embedding with More and Better Negative Samples

Reference 24

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Observation 2d0aac5f-c376-4b26-9d67-58b0fc95885d · outbound

This paper cites ”An information-theoretic perspective of tf–id f measures.” Infor- mation Processing & Management 39, no.

QZhou-Embedding Technical Report ”An information-theoretic perspective of tf–id f measures.” Infor- mation Processing & Management 39, no

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.869778Z

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This paper cites an unresolved cited work.

QZhou-Embedding Technical Report Unresolved cited work

Reference 26

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Observation d53a8bdf-b7a6-400c-9732-736133a59ac0 · outbound

This paper cites Dumais, George W.

QZhou-Embedding Technical Report Dumais, George W

Reference 27

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Observation c43a5a14-6411-4d25-86cf-fdcfc809cd97 · outbound

This paper cites Improving Text Embeddings with Large Language Models.

QZhou-Embedding Technical Report Improving Text Embeddings with Large Language Models

Reference 28

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Observation c183dbbd-3ac3-4581-98f1-bc5f291accfd · outbound

This paper cites ”Sfrembedding-mistral: enhance text retrieval with tran sfer learning.” Sales- force AI Research Blog 3 (2024): 6.

QZhou-Embedding Technical Report ”Sfrembedding-mistral: enhance text retrieval with tran sfer learning.” Sales- force AI Research Blog 3 (2024): 6

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.843554Z

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Observation 78592c53-619a-4a72-b3a4-3274494fc712 · outbound

This paper cites Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024[J].

QZhou-Embedding Technical Report Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024[J]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.834523Z

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Observation 3ea0d1ba-533e-483b-bbf8-2d7e285afb36 · outbound

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QZhou-Embedding Technical Report Unresolved cited work

Reference 31

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Observation d09ae5a4-ffd1-452e-9cbb-dab7f4086158 · outbound

This paper cites Making Text Embedders Few-Shot Learners.

QZhou-Embedding Technical Report Making Text Embedders Few-Shot Learners

Reference 32

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Observation 173f01a6-967e-42ca-96e5-840521963619 · outbound

This paper cites Towards general text embeddings with multi-stage con trastive learning,.

QZhou-Embedding Technical Report Towards general text embeddings with multi-stage con trastive learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.816337Z

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Observation a7d22034-0b06-456d-a7fc-0b32bb899ecd · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

QZhou-Embedding Technical Report Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:08.268012Z

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Observation 04944c12-a85c-46fd-bcc2-720b1ecaa017 · outbound

This paper cites ”Roformer: Enhanced transformer with rotary position embeddin g.” Neurocomput- ing 568 (2024): 127063.

QZhou-Embedding Technical Report ”Roformer: Enhanced transformer with rotary position embeddin g.” Neurocomput- ing 568 (2024): 127063

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.807366Z

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Observation 41172868-1b73-4437-bdb9-743d227129b1 · outbound

This paper cites ”Root mean square layer norma lization.” Ad- vances in neural information processing systems 32 (2019).

QZhou-Embedding Technical Report ”Root mean square layer norma lization.” Ad- vances in neural information processing systems 32 (2019)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.798556Z

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Observation 46f1ed36-0f12-4cd0-aec1-30cec5c443ce · outbound

This paper cites GLU Variants Improve Transformer.

QZhou-Embedding Technical Report GLU Variants Improve Transformer

Reference 37

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Observation 3ea0fc00-e294-478c-a0c0-cb4695ce7eff · outbound

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QZhou-Embedding Technical Report Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.789412Z

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Observation 2b99477a-74ae-4035-a32f-987f9ea849a9 · outbound

This paper cites Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training.

QZhou-Embedding Technical Report Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:11:10.053158Z

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Observation a4ca7093-5265-49fb-8007-4711e6d5c52f · outbound

This paper cites ”Circle loss: A uniﬁed perspective of pair similarit y op- timization.” In Proceedings of the IEEE/CVF conference on comput er vision and pattern recognition, pp.

QZhou-Embedding Technical Report ”Circle loss: A uniﬁed perspective of pair similarit y op- timization.” In Proceedings of the IEEE/CVF conference on comput er vision and pattern recognition, pp

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.780699Z

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Observation 5d74fbfc-fbce-4d29-b3f1-25c7e3ff6f6b · outbound

This paper cites Document Expansion by Query Prediction.

QZhou-Embedding Technical Report Document Expansion by Query Prediction

Reference 41

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Reference 42

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QZhou-Embedding Technical Report Unresolved cited work

Reference 43

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QZhou-Embedding Technical Report Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.753688Z

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Observation 180efc72-158e-4b47-a399-35af33c265eb · outbound

This paper cites Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor.

QZhou-Embedding Technical Report Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor

Reference 45

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Observation 607c8540-e1d4-47a1-a5ec-ab0525ac5f04 · outbound

This paper cites ”Approximate nearest neighbo r negative con- trastive learning for dense text retrieval.” arXiv preprint arXiv:200 7.00808 (2020).

QZhou-Embedding Technical Report ”Approximate nearest neighbo r negative con- trastive learning for dense text retrieval.” arXiv preprint arXiv:200 7.00808 (2020)

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.904446Z

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Observation 17191518-9d7a-4ec5-b2ee-892c3d85825e · outbound

This paper cites NV-Retriever: Improving text embedding models with effective hard-negative mining.

QZhou-Embedding Technical Report NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 47

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Observation af3e5c45-f6da-4f4f-8f3d-e6da638ac530 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

QZhou-Embedding Technical Report Representation Learning with Contrastive Predictive Coding

Reference 48

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Observation 0c7d0faf-5d2e-460a-8732-d59101ed5d44 · outbound

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QZhou-Embedding Technical Report Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.744535Z

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Observation 22d07efe-5df5-4a7f-a42b-fddcf1a9f796 · outbound

This paper cites mgte: Generalized long-context text representation and reranking models for multilingual text retrieval, 2024.

QZhou-Embedding Technical Report mgte: Generalized long-context text representation and reranking models for multilingual text retrieval, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.735012Z

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Observation 2254bab3-e306-4d21-a72d-fbadd1b67d48 · outbound

This paper cites Gecko: Versatile Text Embeddings Distilled from Large Language Models.

QZhou-Embedding Technical Report Gecko: Versatile Text Embeddings Distilled from Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:09.783908Z

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Observation fad817b6-7da1-4170-8e81-db1b6ee0398a · outbound

This paper cites Linq-embed-mistral: Elevat ing text re- trieval with improved gpt data through task-speciﬁc control and quality reﬁnement.

QZhou-Embedding Technical Report Linq-embed-mistral: Elevat ing text re- trieval with improved gpt data through task-speciﬁc control and quality reﬁnement

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.723961Z

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Observation b006dd14-dc9f-4536-80c5-fb6c8eca3f90 · outbound

This paper cites an unresolved cited work.

QZhou-Embedding Technical Report Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.713234Z

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Observation 70a7993c-b5ee-48d5-9e1b-5980184aa81d · outbound

This paper cites An overview of the BIOASQ large- scale biomedical semantic indexing and question answering competitio n[J].

QZhou-Embedding Technical Report An overview of the BIOASQ large- scale biomedical semantic indexing and question answering competitio n[J]

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.703504Z

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Observation 40f20082-60a2-4ee9-bdab-b3004f4826e7 · outbound

This paper cites A Span-Extraction Dataset for Chinese Machine Reading Comprehension.

QZhou-Embedding Technical Report A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:11:09.987987Z

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Observation 95771c6c-27aa-49bd-9e83-640ec7dfc656 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

QZhou-Embedding Technical Report GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:09.798928Z

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Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.693813Z

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Observation 18db73df-4398-49d1-b2b1-1912f3db1487 · outbound

This paper cites Learning word vectors for sent iment analy- sis[C]//Proceedings of the 49th annual meeting of the association f or computational linguistics: Human language technologies.

QZhou-Embedding Technical Report Learning word vectors for sent iment analy- sis[C]//Proceedings of the 49th annual meeting of the association f or computational linguistics: Human language technologies

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.684462Z

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Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.675485Z

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Reference 60

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This paper cites ”Lcqmc: A large-scale chinese question matching corpus.” In Proceedings of the 27th international conference on computatio nal linguistics, pp.

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Reference 61

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This paper cites PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification.

QZhou-Embedding Technical Report PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification

Reference 62

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Unavailable: canonical work link unavailable.

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Reference 63

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Reference 64

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Observation 9667e35d-ca5b-4c21-b0dd-f42052436938 · outbound

This paper cites Natural questions: a benchmark for question answering res earch.

QZhou-Embedding Technical Report Natural questions: a benchmark for question answering res earch

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.636451Z

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Reference 66

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Reference 67

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Observation 36396d0d-f380-4ea8-83ed-0fb4a3147ce2 · outbound

This paper cites Miracl: A multilingual retrieval dataset covering 18 diverse language s.

QZhou-Embedding Technical Report Miracl: A multilingual retrieval dataset covering 18 diverse language s

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.614855Z

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Observation f676c3fa-42a1-4042-a553-9f76dcb120b5 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

QZhou-Embedding Technical Report SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 69

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Unavailable: canonical work link unavailable.

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QZhou-Embedding Technical Report FEVER: a large-scale dataset for Fact Extraction and VERification

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:09.842916Z

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Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.604876Z

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Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.584590Z

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This paper cites Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval.

QZhou-Embedding Technical Report Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

Reference 73

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Reference 74

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Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.565283Z

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Observation 2d994733-bb24-41a1-8321-db07b9a3b221 · outbound

This paper cites Gemini Embedding: Generalizable Embeddings from Gemini.

QZhou-Embedding Technical Report Gemini Embedding: Generalizable Embeddings from Gemini

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:09.865567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.554505Z

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Observation e9810b70-fcef-4990-b69e-6359a6f383df · outbound

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Reference 1620

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:11:10.543841Z

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Observation 029777a9-79d2-4f12-93af-ef40dff6bc2d · outbound

This paper cites In Proceedings of the Workshop on Machine Read ing for Ques- tion Answering, pages 37–46, Melbourne, Australia.

QZhou-Embedding Technical Report In Proceedings of the Workshop on Machine Read ing for Ques- tion Answering, pages 37–46, Melbourne, Australia

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:10.595702Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 247ae6e0-da90-4f66-9886-174c7bf52ab1 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

QZhou-Embedding Technical Report Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:08.141738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers