mlsanityData to 7 Oct 2026

Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights

arXiv 2310.12462 ↗First posted 19 Oct 2023cs.LG cs.CL stat.MLcited by 11

Not on the map. Why?

Abstract (arXiv)

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be recovered using their attention weights and outputs? We introduce a theoretical framework to tackle this problem. Specifically, we present an algorithm that aims to recover the input data X ∈ ℝd × n from given attention weights W = QK^top ∈ ℝd × d and output B ∈ ℝn × n by minimizing the loss function L(X). This loss function captures the discrepancy between the expected output and the actual output of the transformer. Our findings have significant implications for the Localized Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's design from a security and privacy perspective. This work underscores the importance of understanding and safeguarding the internal workings of transformers to ensure the confidentiality of processed data.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 73 of its 113 references that are in the map. First posted on arXiv 19 Oct 2023.

Builds on

73 of 113 references.

References · 73

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  7. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  8. 2020Reformer: The Efficient Transformercited by 1,658
  9. 2023BloombergGPT: A Large Language Model for Financecited by 923
  10. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 784
  11. 2020Long Range Arena: A Benchmark for Efficient Transformerscited by 696
  12. 2023A Watermark for Large Language Modelscited by 667
38 more
  1. 2018Gradient Descent Provably Optimizes Over-parameterized Neural Networkscited by 655
  2. 2019Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networkscited by 562
  3. 2023Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruningcited by 408
  4. 2019CamemBERT: a Tasty French Language Modelcited by 368
  5. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  6. 2023Fine-Tuning Language Models with Just Forward Passescited by 337
  7. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  8. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  9. 2023Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-trainingcited by 260
  10. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  11. 2023Representational Strengths and Limitations of Transformerscited by 132
  12. 2019Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networkscited by 120
  13. 2022A Kernel-Based View of Language Model Fine-Tuningcited by 118
  14. 2016Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanismcited by 117
  15. 2023A Theory for Emergence of Complex Skills in Language Modelscited by 111
  16. 2023Task-Specific Skill Localization in Fine-tuned Language Modelscited by 104
  17. 2023Fast Attention Requires Bounded Entriescited by 102
  18. 2023On Provable Copyright Protection for Generative Modelscited by 97
  19. 2020InstaHide: Instance-hiding Schemes for Private Distributed Learningcited by 90
  20. 2023How Do Transformers Learn Topic Structure: Towards a Mechanistic Understandingcited by 85
  21. 2022CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarkscited by 68
  22. 2023Transformers as Support Vector Machinescited by 67
  23. 2023Federated Learning of Gboard Language Models with Differential Privacycited by 64
  24. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 63
  25. 2021Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Timecited by 62
  26. 2019Quadratic Suffices for Over-parametrization via Matrix Chernoff Boundcited by 58
  27. 2020Training (Overparametrized) Neural Networks in Near-Linear Timecited by 58
  28. 2023KDEformer: Accelerating Transformers via Kernel Density Estimationcited by 58
  29. 2023Attention Scheme Inspired Softmax Regressioncited by 49
  30. 2021A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimizationcited by 46
  31. 2023An Over-parameterized Exponential Regressioncited by 45
  32. 2020Faster Dynamic Matrix Inverse for Faster LPscited by 44
  33. 2023Solving Regularized Exp, Cosh and Sinh Regression Problemscited by 44
  34. 2016Defeating Image Obfuscation with Deep Learningcited by 43
  35. 2023The Closeness of In-Context Learning and Weight Shifting for Softmax Regressioncited by 41
  36. 2019Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problemscited by 40
  37. 2021Approximating How Single Head Attention Learnscited by 38
  38. 2023Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Modelscited by 37

50 most cited of 73 All 73 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 7; 2025: 2.

11 papers.

  1. 2024LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformerscited by 35
  2. 2024Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformerscited by 32
  3. 2024Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Timecited by 22
  4. 2025Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolationcited by 11
  5. 2025Gating is Weighting: Understanding Gated Linear Attention through In-context Learningcited by 11
  6. 2024Towards Infinite-Long Prefix in Transformercited by 10
  7. 2024Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selectioncited by 9
  8. 2023A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformercited by 6
  9. 2024Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Studycited by 4
  10. 2023Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Trainingcited by 0
  11. 2024Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergencecited by 0