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arxiv: 2506.05176 · v3 · submitted 2025-06-05 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Yanzhao Zhang , Mingxin Li , Dingkun Long , Xin Zhang , Huan Lin , Baosong Yang , Pengjun Xie , An Yang
show 4 more authors
Dayiheng Liu Junyang Lin Fei Huang Jingren Zhou
Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:41 UTC · model grok-4.3

classification 💻 cs.CL
keywords text embeddingrerankingmultilingual retrievalfoundation modelsdata synthesismodel mergingcross-lingual retrieval
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The pith

Qwen3 Embedding models set new highs on multilingual text retrieval by synthesizing their own training data from the Qwen3 foundation models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the Qwen3 Embedding series as an advance over earlier GTE-Qwen models for turning text into useful vectors and for reranking search results. It builds these systems directly on the Qwen3 large language models through a multi-stage process of large-scale unsupervised pre-training, supervised fine-tuning on datasets generated by the same models, and model merging. The series comes in three sizes (0.6 billion, 4 billion, and 8 billion parameters) that support both embedding and reranking, letting users trade speed for accuracy. A sympathetic reader would care because better embeddings improve search engines, recommendation systems, and analysis tools that must work across many languages and domains at once.

Core claim

The Qwen3 Embedding series, built on Qwen3 foundation models in sizes 0.6B, 4B, and 8B, reaches state-of-the-art results on the multilingual MTEB benchmark for text embedding as well as on code retrieval, cross-lingual retrieval, and multilingual retrieval tasks. This performance comes from a training pipeline that uses the Qwen3 models both as the core architecture and as the source for synthesizing high-quality, diverse training data across domains and languages, followed by supervised fine-tuning and model merging to improve robustness.

What carries the argument

Multi-stage training pipeline that treats the Qwen3 LLMs as both backbone models and generators of rich, domain-specific training data, combined with model merging after supervised fine-tuning.

If this is right

  • Developers can select among the three model sizes to match available compute when deploying embedding or reranking systems.
  • The same pipeline supports stronger performance on code search and cross-language document retrieval without custom architectures for each task.
  • Public release of the models under Apache 2.0 license enables direct use and further fine-tuning by the community.
  • The approach shows that foundation models can generate the data needed to specialize themselves for embedding tasks across many languages.
  • Model merging after fine-tuning provides a practical way to combine strengths from different training stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If self-synthesis of training data works reliably, it could reduce dependence on large manually labeled datasets for future embedding models.
  • The results suggest that scaling the same family of models for both generation and embedding may create tighter feedback loops than using separate models for each role.
  • Testing the models on entirely held-out languages or domains not used in the synthesis step would clarify how far the generalization extends.
  • Similar data-generation and merging steps could be tried on other open foundation models to see whether the performance pattern repeats.

Load-bearing premise

The reported gains reflect real generalization from the training methods rather than overlap with benchmark data or overfitting to known test sets.

What would settle it

Evaluating the released models on a new retrieval benchmark created after the paper's release, with no possible overlap to the synthesized training data, and checking whether performance remains at the claimed level.

read the original abstract

In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models. Leveraging the Qwen3 LLMs' robust capabilities in multilingual text understanding and generation, our innovative multi-stage training pipeline combines large-scale unsupervised pre-training with supervised fine-tuning on high-quality datasets. Effective model merging strategies further ensure the robustness and adaptability of the Qwen3 Embedding series. During the training process, the Qwen3 LLMs serve not only as backbone models but also play a crucial role in synthesizing high-quality, rich, and diverse training data across multiple domains and languages, thus enhancing the training pipeline. The Qwen3 Embedding series offers a spectrum of model sizes (0.6B, 4B, 8B) for both embedding and reranking tasks, addressing diverse deployment scenarios where users can optimize for either efficiency or effectiveness. Empirical evaluations demonstrate that the Qwen3 Embedding series achieves state-of-the-art results across diverse benchmarks. Notably, it excels on the multilingual evaluation benchmark MTEB for text embedding, as well as in various retrieval tasks, including code retrieval, cross-lingual retrieval and multilingual retrieval. To facilitate reproducibility and promote community-driven research and development, the Qwen3 Embedding models are publicly available under the Apache 2.0 license.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the Qwen3 Embedding series, built on Qwen3 foundation models, as an advance over GTE-Qwen for text embedding and reranking. It describes a multi-stage training pipeline that combines large-scale unsupervised pre-training, supervised fine-tuning on high-quality datasets synthesized by the Qwen3 LLMs themselves, and model merging strategies. Models are released in 0.6B, 4B, and 8B sizes for both embedding and reranking, with the central claim being state-of-the-art results on the multilingual MTEB benchmark as well as on code retrieval, cross-lingual retrieval, and multilingual retrieval tasks. The models are made publicly available under Apache 2.0.

Significance. If the empirical claims hold after verification, the work would demonstrate a practical way to leverage the same foundation LLM family for both backbone architecture and training-data synthesis, yielding gains in multilingual and retrieval settings. The public release of the model weights is a clear strength that supports reproducibility and community follow-up work.

major comments (2)
  1. [Abstract and Evaluation sections] Abstract and Evaluation sections: The manuscript asserts SOTA performance on MTEB multilingual embedding and multiple retrieval tasks, yet the abstract supplies no quantitative scores, baseline comparisons, ablation results, or evaluation-protocol details. Without these, it is impossible to determine whether the reported gains are statistically meaningful or free of common confounds such as test-set leakage.
  2. [Training pipeline description] Training pipeline description: The multi-stage pipeline uses Qwen3 LLMs both as the backbone and to synthesize the high-quality, diverse training data. No decontamination, n-gram overlap filtering, or membership-inference checks against MTEB test sets or retrieval corpora are described. Because the same model family generates the training examples, any overlap would produce inflated scores without genuine generalization; this directly undermines the central SOTA claim.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one or two concrete performance numbers (e.g., MTEB average score and the strongest baseline) to allow readers to gauge the magnitude of the advance immediately.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which have helped clarify the presentation of our results and the rigor of our training pipeline description. We address each major comment below and have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and Evaluation sections] Abstract and Evaluation sections: The manuscript asserts SOTA performance on MTEB multilingual embedding and multiple retrieval tasks, yet the abstract supplies no quantitative scores, baseline comparisons, ablation results, or evaluation-protocol details. Without these, it is impossible to determine whether the reported gains are statistically meaningful or free of common confounds such as test-set leakage.

    Authors: We agree that the abstract would benefit from explicit quantitative results to allow readers to immediately assess the claimed improvements. In the revised manuscript we have updated the abstract to include key MTEB multilingual scores, comparisons against GTE-Qwen and other strong baselines, and a concise statement of the evaluation protocol. The Evaluation section already reports full results, ablations, and protocol details; we have added further text on statistical significance testing and explicit checks for test-set leakage to strengthen this part of the paper. revision: yes

  2. Referee: [Training pipeline description] Training pipeline description: The multi-stage pipeline uses Qwen3 LLMs both as the backbone and to synthesize the high-quality, diverse training data. No decontamination, n-gram overlap filtering, or membership-inference checks against MTEB test sets or retrieval corpora are described. Because the same model family generates the training examples, any overlap would produce inflated scores without genuine generalization; this directly undermines the central SOTA claim.

    Authors: The referee correctly notes that the original manuscript did not explicitly describe decontamination steps. We have added a dedicated paragraph in the Training Pipeline section that details the decontamination process: n-gram overlap filtering (with a conservative threshold) was applied to remove any potential overlap with MTEB test sets and retrieval corpora, and membership-inference-style checks were performed on the synthesized data. These steps were part of the data-preparation pipeline and ensure that the reported gains reflect generalization rather than leakage. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks

full rationale

The paper presents an empirical ML engineering effort: a multi-stage training pipeline (unsupervised pre-training + supervised fine-tuning + model merging) on data synthesized by Qwen3 LLMs, with final performance measured on external benchmarks such as MTEB and various retrieval tasks. No mathematical derivation chain, first-principles equations, or 'predictions' exist that could reduce to the inputs by construction. No self-definitional steps, fitted parameters renamed as predictions, uniqueness theorems imported via self-citation, or ansatz smuggling appear. The SOTA claims are supported by reported benchmark numbers rather than any closed-loop logic internal to the paper. Minor self-citations to prior Qwen/GTE work are present but not load-bearing for the central result, which remains independently falsifiable via the external evaluations.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on the assumption that Qwen3 LLMs possess strong multilingual and generative capabilities that transfer to embedding tasks, plus standard supervised learning assumptions about data quality and generalization. No new mathematical axioms or invented physical entities are introduced.

free parameters (2)
  • model sizes
    0.6B, 4B, 8B variants chosen to cover efficiency-effectiveness trade-offs
  • training hyperparameters
    Unspecified details of the multi-stage pipeline and merging weights
axioms (2)
  • domain assumption Qwen3 LLMs have robust capabilities in multilingual text understanding and generation
    Invoked as the foundation for both backbone use and data synthesis
  • domain assumption High-quality, diverse training data can be synthesized by the same LLMs across domains and languages
    Central to the multi-stage pipeline described in the abstract

pith-pipeline@v0.9.0 · 5589 in / 1361 out tokens · 40632 ms · 2026-05-10T13:41:56.602817+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages · cited by 130 Pith papers · 6 internal anchors

  1. [1]

    M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

    Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. In Findings of the Association for Computational Linguistics: ACL 2024 , pp. 2318–2335, Bangkok, Thailand, August

    work page 2024

  2. [2]

    URL https:// aclanthology.org/2024.findings-acl.137/

    Association for Computational Linguistics. URL https:// aclanthology.org/2024.findings-acl.137/. Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, M´arton Kardos, Ashwin Mathur, David Stap, Jay Gala, Wissam Siblini, Dominik Krzemi ´nski, Genta Indra Winata, et al. MMTEB: Massive multilingual text embedding benchmark. In The Thirteenth International Conferen...

    work page 2024

  3. [3]

    Scaling synthetic data creation with 1,000,000,000 personas.arXiv:2406.20094, 2024

    URL https://openreview.net/forum?id=zl3pfz4VCV. Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. Scaling synthetic data creation with 1,000,000,000 personas. arXiv preprint arXiv:2406.20094,

    work page arXiv

  4. [4]

    GPT-4o System Card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276,

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

    Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. Nv-embed: Improved techniques for training llms as generalist embedding models. arXiv preprint arXiv:2405.17428,

    work page internal anchor Pith review arXiv

  6. [6]

    Gemini Embedding: Generalizable Embeddings from Gemini

    Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. NV-embed: Improved techniques for training LLMs as generalist embedding models. In The Thirteenth International Conference on Learning Representations , 2025a. URL https: //openreview.net/forum?id=lgsyLSsDRe. Jinhyuk Lee, Feiyang Chen, Sahil Dua, Danie...

    work page internal anchor Pith review arXiv

  7. [7]

    Towards General Text Embeddings with Multi-stage Contrastive Learning

    URL https://arxiv.org/ abs/2308.03281. Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. Zero-shot listwise document reranking with a large language model. arXiv preprint arXiv:2305.02156,

    work page internal anchor Pith review arXiv

  8. [8]

    MTEB: Massive text embed- ding benchmark

    Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers. MTEB: Massive text embed- ding benchmark. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 2014–2037, Dubrovnik, Croatia, May

    work page 2014

  9. [9]

    URL https://aclanthology.org/2023.eacl-main.148/

    Association for Computa- tional Linguistics. URL https://aclanthology.org/2023.eacl-main.148/. Niklas Muennighoff, Hongjin SU, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela. Generative representational instruction tuning. In The Thirteenth International Con- ference on Learning Representations,

    work page 2023

  10. [10]

    Representation Learning with Contrastive Predictive Coding

    URL https://openreview.net/forum?id=BC4lIvfSzv. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predic- tive coding. arXiv preprint arXiv:1807.03748,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    arXiv preprint arXiv:2309.15088 , year=

    Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. Rankvicuna: Zero-shot listwise docu- ment reranking with open-source large language models. arXiv preprint arXiv:2309.15088,

    work page arXiv

  12. [12]

    Sentence-BERT: Sentence embeddings using Siamese BERT- networks

    Nils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT- networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , pp. 3982–3992, Hong Kong, China, November

    work page 2019

  13. [13]

    URL https://aclanthology.org/D19-1410/

    Association for Computational Linguistics. URL https://aclanthology.org/D19-1410/. Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu. One embedder, any task: Instruction-finetuned text embeddings. In Findings of the Association for Computational Linguistics: ACL 2023 , pp. ...

    work page 2023

  14. [14]

    Text Embeddings by Weakly-Supervised Contrastive Pre-training

    URL https://arxiv.org/abs/2212.03533. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. Im- proving text embeddings with large language models. In Proceedings of the 62nd Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers) , pp. 11897–11916, Bangkok, Thailand, August

    work page internal anchor Pith review arXiv

  15. [15]

    URL https: //aclanthology.org/2024.acl-long.642/

    Association for Computational Linguistics. URL https: //aclanthology.org/2024.acl-long.642/. Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, and Luca Soldaini. Followir: Evaluating and teaching information retrieval models to follow instructions. arXiv preprint arXiv:2403.15246,

    work page arXiv 2024

  16. [16]

    InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval(Washington DC, USA)(SIGIR ’24)

    Association for Computing Machinery. URLhttps://doi.org/10.1145/ 3626772.3657878. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388,

    work page arXiv

  17. [17]

    A two-stage adaptation of large language models for text ranking

    10 Technical Report Longhui Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, and Min Zhang. A two-stage adaptation of large language models for text ranking. In Findings of the Association for Computational Linguistics ACL 2024 , pp. 11880–11891, 2024a. Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baoso...

    work page 2024

  18. [18]

    Embedding in recommender systems: A survey

    Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, and Ruocheng Guo. Embedding in recommender systems: A survey. arXiv preprint arXiv:2310.18608,

    work page arXiv

  19. [19]

    Question Type

    11 Technical Report A Appendix A.1 Synthetic Data We construct four types of synthetic data—retrieval, bitext mining, semantic textual similarity, and classification to enable the model to adapt to various similarity tasks during pre-training. To ensure both multilingual and cross-lingual diversity, the data is generated using Qwen3 32B. Below is an examp...

    work page 2024

  20. [20]

    (MTEB(cmn, v1). MTEB(Code, v1) Avg.Apps COIR-CodeSearch-Net Code-Edit-Search Code-Feedback-MT Code-Feedback-ST Code-SearchNet-CCR Code-SearchNet Code-Trans-Ocean-Contest Code-Trans-Ocean-DLCosQAStack-Overflow-QA Synthetic-Text2SQL BGEmultilingual 62.0422.93 68.14 60.48 60.52 76.70 73.23 83.43 86.84 32.64 27.93 92.93 58.67NV-Embed-v2 63.7429.72 61.85 73.96...

    work page 2094