mlsanityData to 7 Oct 2026

C-Pack: Packed Resources For General Chinese Embeddings

arXiv 2309.07597 ↗First posted 14 Sep 2023cs.CL cs.AI cs.IRcited by 556

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Cited by 141 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 2) C-MTP is a massive text embedding dataset curated from labeled and unlabeled Chinese corpora for training embedding models. 3) C-TEM is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by up to +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for C-TEM. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models achieve state-of-the-art performance on MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.

Key facts. Cited by 556 papers in the map (data through 7 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

39 of 63 references.

References · 39

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  8. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  9. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  10. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  11. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  12. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
27 more
  1. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  4. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  5. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  6. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  7. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  8. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  9. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  10. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 410
  11. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 358
  12. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 329
  13. 2018SentEval: An Evaluation Toolkit for Universal Sentence Representationscited by 294
  14. 2020CLUE: A Chinese Language Understanding Evaluation Benchmarkcited by 277
  15. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  16. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  17. 2021Scaling Deep Contrastive Learning Batch Size under Memory Limited Setupcited by 123
  18. 2021mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datasetcited by 116
  19. 2017DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applicationscited by 110
  20. 2022SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrievalcited by 90
  21. 2023T2Ranking: A large-scale Chinese Benchmark for Passage Rankingcited by 59
  22. 2022Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrievalcited by 26
  23. 2022Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Searchcited by 17
  24. 2022Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddingscited by 17
  25. 2023Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboardcited by 15
  26. 2023RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Modelscited by 13
  27. 2022Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrievalcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 63; 2025: 184; 2026: 308.

556 papers.

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  1. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  2. 2024OLMoE: Open Mixture-of-Experts Language Modelscited by 291
  3. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  4. 2024Generative Representational Instruction Tuningcited by 227
  5. 2024ToolACE: Winning the Points of LLM Function Callingcited by 204
  6. 2024GME: Improving Universal Multimodal Retrieval by Multimodal LLMscited by 166
  7. 2024Making Text Embedders Few-Shot Learnerscited by 78
  8. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  9. 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 76
  10. 2025DeepAgent: A General Reasoning Agent with Scalable Toolsetscited by 69
  11. 2026SkillRouter: Skill Routing for LLM Agents at Scalecited by 57
  12. 2025ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenariocited by 53
38 more
  1. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systemscited by 50
  2. 2025mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Datacited by 49
  3. 2026SkillNet: Create, Evaluate, and Connect AI Skillscited by 48
  4. 2026Skill Retrieval Augmentation for Agentic AIcited by 41
  5. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Frameworkcited by 39
  6. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learningcited by 39
  7. 2026SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agentscited by 37
  8. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 35
  9. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  10. 2024RAR-b: Reasoning as Retrieval Benchmarkcited by 33
  11. 2024CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrievalcited by 33
  12. 2024LLMs are Also Effective Embedding Models: An In-depth Overviewcited by 32
  13. 2024Polaris: A Safety-focused LLM Constellation Architecture for Healthcarecited by 27
  14. 2025A Survey of Personalization: From RAG to Agentcited by 26
  15. 2025BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLPcited by 26
  16. 2025MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learningcited by 26
  17. 2025SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reductioncited by 24
  18. 2025MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosiscited by 23
  19. 2024RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generationcited by 20
  20. 2025Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agentscited by 20
  21. 2025MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddingscited by 20
  22. 2024PL-MTEB: Polish Massive Text Embedding Benchmarkcited by 19
  23. 2025DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrieverscited by 19
  24. 2025R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learningcited by 19
  25. 2025DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruningcited by 19
  26. 2023Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuningcited by 18
  27. 2024SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Taskscited by 18
  28. 2024Linq-Embed-Mistral Technical Reportcited by 18
  29. 2025UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularitiescited by 18
  30. 2025Language Models Improve When Pretraining Data Matches Target Taskscited by 18
  31. 2024Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Basescited by 17
  32. 2026Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preferencecited by 17
  33. 2024Enabling Discriminative Reasoning in LLMs for Legal Judgment Predictioncited by 16
  34. 2025Hierarchical Document Refinement for Long-context Retrieval-augmented Generationcited by 16
  35. 2025Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systemscited by 16
  36. 2024Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalabilitycited by 15
  37. 2024JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resourcescited by 15
  38. 2025O1 Embedder: Let Retrievers Think Before Actioncited by 15

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