---
title: "Text Embeddings Reveal (Almost) As Much As Text — paper by Morris et al. (2023)"
description: "The paper asks whether dense text embeddings, such as those stored in hosted vector databases, can… 0% exact match. 56 references, cited by 56 papers here."
authors: ["John Morris", "Volodymyr Kuleshov", "Vitaly Shmatikov", "Alexander Rush"]
year: 2023
venue: "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2023.emnlp-main.765/"
doi: "10.18653/v1/2023.emnlp-main.765"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/morris-2023-text-embeddings-reveal-almost-as/"
---
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# Text Embeddings Reveal (Almost) As Much As Text

**Authors:** [John Morris](https://md.research.elamur.ai/free/en/authors/john-x-morris/), [Volodymyr Kuleshov](https://md.research.elamur.ai/free/en/authors/volodymyr-kuleshov/), [Vitaly Shmatikov](https://md.research.elamur.ai/free/en/authors/vitaly-shmatikov/) and [Alexander Rush](https://md.research.elamur.ai/free/en/authors/alexander-m-rush/) · **Year:** 2023 · **Venue:** [Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2023](https://md.research.elamur.ai/free/en/venues/emnlp/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.emnlp-main.765/) · [PDF](https://aclanthology.org/2023.emnlp-main.765.pdf) · [DOI 10.18653/v1/2023.emnlp-main.765](https://doi.org/10.18653/v1/2023.emnlp-main.765) · [OpenAlex](https://openalex.org/W4389520346)

**Citations:** 90 citations (OpenAlex, 2026-10-14) · [56](https://md.research.elamur.ai/free/en/papers/morris-2023-text-embeddings-reveal-almost-as/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks whether dense text embeddings, such as those stored in hosted vector databases, can be turned back into the original text. The authors treat inversion as controlled generation: an attacker who can only query a black-box encoder searches for text whose embedding matches a target vector. Their method, Vec2Text, starts from an initial guess produced by a conditional T5-style encoder-decoder. It then repeatedly re-embeds that guess and feeds the target embedding, the guess's embedding and their difference back into a correction model, using sequence-level beam search over successive edits. The models invert GTR-base, trained on 5M Wikipedia passages from Natural Questions, and OpenAI text-embeddings-ada-002, trained on MSMARCO. Evaluation covers in-domain data, BEIR and MIMIC-III clinical notes. Iterative correction far outperforms one-shot decoding and recovers most short passages verbatim. Because personal details such as patient names can be extracted, the authors argue that embeddings deserve the same privacy safeguards as the raw text they encode.

## Key points
- On 32-token Natural Questions passages embedded with GTR-base, 50 correction steps plus sequence beam search reach BLEU 97.3 and 92% exact recovery. The uncorrected base model reaches BLEU 31.9 and 0% exact matches.
- For text-embeddings-ada-002 on MSMARCO, exact recovery is 60.9% for 32-token inputs. For 128-token inputs it falls to 8.0%, with BLEU 55.0.
- On MIMIC-III clinical notes, the method recovers 89% of full names.
- Earlier baselines perform far worse, including the bag-of-words inversion of Song and Raghunathan (2020) at BLEU 0.3. Results keep improving with more search rounds and wider beams (tested up to 50 rounds and beam width 8).
- Code is released as vec2text on GitHub. Embedding 5M documents through the OpenAI API cost about $35 at 2023 prices, which makes such attacks inexpensive.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Cornell University](https://md.research.elamur.ai/free/en/universities/cornell-university/) — University, Ithaca, United States · 89 works · 58,422 citations

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## Cite

Cite as: John Morris, Volodymyr Kuleshov, Vitaly Shmatikov and Alexander Rush. 2023. Text Embeddings Reveal (Almost) As Much As Text. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{morris2023text,
  title = {{Text Embeddings Reveal (Almost) As Much As Text}},
  author = {John Morris, Volodymyr Kuleshov, Vitaly Shmatikov and Alexander Rush},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023},
  doi = {10.18653/v1/2023.emnlp-main.765},
  url = {https://doi.org/10.18653/v1/2023.emnlp-main.765}
}
```

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- [next → Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/5/)

## References

56 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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49. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2023. Simlm: Pre-training with representation bottleneck for dense passage retrieval. [our card](https://md.research.elamur.ai/free/en/papers/wang-2023-simlm-pre-training-representation-bottleneck/)
50. Weaviate. 2023. Weaviate - vector database. [find](https://scholar.google.com/scholar?q=Weaviate%2E+2023%2E+Weaviate+%2D+vector+database%2E)
51. Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2022. Generating sequences by learning to self-correct. [our card](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/)
52. Shitao Xiao, Zheng Liu, Yingxia Shao, and Zhao Cao. 2022. Retromae: Pre-training retrieval-oriented language models via masked auto-encoder. [our card](https://md.research.elamur.ai/free/en/papers/xiao-2022-retromae-pre-training-retrieval-oriented/)
53. Kevin Yang and Dan Klein. 2021. FUDGE: Controlled text generation with future discriminators. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. [our card](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
54. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. [our card](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/)
55. Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. idlg: Improved deep leakage from gradients. [find](https://scholar.google.com/scholar?q=Bo+Zhao%2C+Konda+Reddy+Mopuri%2C+and+Hakan+Bilen%2E+2020%2E+idlg%3A+Improved+deep+leakage+from+gradients%2E)
56. Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep leakage from gradients. [find](https://scholar.google.com/scholar?q=Ligeng+Zhu%2C+Zhijian+Liu%2C+and+Song+Han%2E+2019%2E+Deep+leakage+from+gradients%2E)

*Updated 24 October 2026*