Extracting Training Data from Diffusion Language Models via Infilling | Paper Citation Record

Observation ad5de18c-1b83-49c6-8833-561b61bfaf62 · outbound

This paper cites https://deepmind.google/models/gemini-diffusion/.

Extracting Training Data from Diffusion Language Models via Infilling https://deepmind.google/models/gemini-diffusion/

Reference 1

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This paper cites Block diffusion: Interpolating between autoregressive and diffusion language models.

Extracting Training Data from Diffusion Language Models via Infilling Block diffusion: Interpolating between autoregressive and diffusion language models

Reference 2

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This paper cites Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993.

Extracting Training Data from Diffusion Language Models via Infilling Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993

Reference 3

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This paper cites LLaDA2.0: Scaling Up Diffusion Language Models to 100B.

Extracting Training Data from Diffusion Language Models via Infilling LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 4

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Observation 13a9a7ef-b8ed-4769-a688-943f7f6674de · outbound

This paper cites LLaDA2.1 : Speeding up text diffusion via token editing.

Extracting Training Data from Diffusion Language Models via Infilling LLaDA2.1 : Speeding up text diffusion via token editing

Reference 5

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Observation 30ed30b9-68c5-460d-b095-fb9ca85f9d7b · outbound

This paper cites Extracting training data from large language models.

Extracting Training Data from Diffusion Language Models via Infilling Extracting training data from large language models

Reference 6

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Observation 8af5b25d-b9ab-4f12-a8da-5adb1f10ac35 · outbound

This paper cites Quantifying memorization across neural language models.

Extracting Training Data from Diffusion Language Models via Infilling Quantifying memorization across neural language models

Reference 7

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Observation 9fbd5af7-0493-4a7d-99fa-e042a0f08671 · outbound

This paper cites Tracedet: Hallucination detection from the decoding trace of diffusion large language models.

Extracting Training Data from Diffusion Language Models via Infilling Tracedet: Hallucination detection from the decoding trace of diffusion large language models

Reference 8

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Observation b85c2f5e-e363-4004-bda9-484b2e929889 · outbound

This paper cites Membership inference attacks against fine-tuned diffusion language models.

Extracting Training Data from Diffusion Language Models via Infilling Membership inference attacks against fine-tuned diffusion language models

Reference 9

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This paper cites an unresolved cited work.

Extracting Training Data from Diffusion Language Models via Infilling Unresolved cited work

Reference 10

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Observation adbe1d80-fb48-4cdf-8ef4-7087ccc8b36c · outbound

This paper cites Partition generative modeling: Masked mod- eling without masks.

Extracting Training Data from Diffusion Language Models via Infilling Partition generative modeling: Masked mod- eling without masks

Reference 11

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This paper cites an unresolved cited work.

Extracting Training Data from Diffusion Language Models via Infilling Unresolved cited work

Reference 12

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Observation 1d5bb6f0-1223-4b29-aa89-630b6546ea52 · outbound

This paper cites Do membership inference attacks work on large language models? InConference on Language Modeling (COLM), 2024.

Extracting Training Data from Diffusion Language Models via Infilling Do membership inference attacks work on large language models? InConference on Language Modeling (COLM), 2024

Reference 13

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This paper cites General data protection regulation, 2016.

Extracting Training Data from Diffusion Language Models via Infilling General data protection regulation, 2016

Reference 14

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Observation 152ce0a3-9a59-4bb2-a931-45944f1c435f · outbound

This paper cites Unlocking Prompt Infilling Capability for Diffusion Language Models.

Extracting Training Data from Diffusion Language Models via Infilling Unlocking Prompt Infilling Capability for Diffusion Language Models

Reference 15

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Observation b1e34da5-824c-4cbf-a40a-a34a1f2ce824 · outbound

This paper cites Scaling diffusion language models via adaptation from autoregressive models.

Extracting Training Data from Diffusion Language Models via Infilling Scaling diffusion language models via adaptation from autoregressive models

Reference 16

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Observation 5cb4e973-a379-4a2b-9f76-960e0bcb17ef · outbound

This paper cites Measuring memorization in language models via probabilistic extraction.

Extracting Training Data from Diffusion Language Models via Infilling Measuring memorization in language models via probabilistic extraction

Reference 17

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Observation d34ea583-524e-4204-8b58-2021ac96569e · outbound

This paper cites Diffusionbert: Improving generative masked language models with diffusion models.

Extracting Training Data from Diffusion Language Models via Infilling Diffusionbert: Improving generative masked language models with diffusion models

Reference 18

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Observation 7693f677-18d9-47c2-a80e-1bbfa5ce1bb4 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Extracting Training Data from Diffusion Language Models via Infilling LoRA: Low-rank adaptation of large language models

Reference 19

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Observation 1ac5e508-39fe-4f3f-9c18-af9fa669a898 · outbound

This paper cites Accelerating diffusion LLMs via adaptive parallel decoding.

Extracting Training Data from Diffusion Language Models via Infilling Accelerating diffusion LLMs via adaptive parallel decoding

Reference 20

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Observation 59724f74-3b18-46f2-9c13-551033d1f287 · outbound

This paper cites Copyright violations and large language models.

Extracting Training Data from Diffusion Language Models via Infilling Copyright violations and large language models

Reference 21

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Observation d9f80b3f-bc1a-47f7-90d3-0612892c6a7f · outbound

This paper cites The enron corpus: A new dataset for email classification research.

Extracting Training Data from Diffusion Language Models via Infilling The enron corpus: A new dataset for email classification research

Reference 22

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Observation 02eb047e-26bf-4f14-9e8e-8e3f137ec510 · outbound

This paper cites Mercury: Ultra-Fast Language Models Based on Diffusion.

Extracting Training Data from Diffusion Language Models via Infilling Mercury: Ultra-Fast Language Models Based on Diffusion

Reference 23

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Observation bc0d10da-e6d9-44ac-9f80-58fda6831790 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Extracting Training Data from Diffusion Language Models via Infilling Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 24

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Observation 7ac6acb0-8872-4655-b9dc-8e98904a0437 · outbound

This paper cites Deduplicating training data makes language mod- els better.

Extracting Training Data from Diffusion Language Models via Infilling Deduplicating training data makes language mod- els better

Reference 25

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Observation 0eb1effe-51b6-46ba-bc9b-98ed92cb0bc5 · outbound

This paper cites Breaking AR’s sampling bottleneck: Provable acceleration via diffusion language models.

Extracting Training Data from Diffusion Language Models via Infilling Breaking AR’s sampling bottleneck: Provable acceleration via diffusion language models

Reference 26

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Observation fcb7cae4-9e8a-4781-a1c1-7b2265257b3e · outbound

This paper cites Diffusion language model knows the answer before it decodes.

Extracting Training Data from Diffusion Language Models via Infilling Diffusion language model knows the answer before it decodes

Reference 27

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Observation 60a2cf7c-5d67-4d3f-b08d-dc8d915566c0 · outbound

This paper cites A Survey on Diffusion Language Models.

Extracting Training Data from Diffusion Language Models via Infilling A Survey on Diffusion Language Models

Reference 28

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Observation 3820ece0-4f0c-4cc5-bedc-6abcba1fb37f · outbound

This paper cites Diffusion-lm improves controllable text generation.Advances in neural information processing systems, 35:4328–4343, 2022.

Extracting Training Data from Diffusion Language Models via Infilling Diffusion-lm improves controllable text generation.Advances in neural information processing systems, 35:4328–4343, 2022

Reference 29

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Observation bd9c8c6c-bdfc-4ef7-999a-ef248610e532 · outbound

This paper cites Analyzing leakage of personally identifiable information in language models.

Extracting Training Data from Diffusion Language Models via Infilling Analyzing leakage of personally identifiable information in language models

Reference 30

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Observation 768e795d-6fee-4f2f-926a-8939a370bcc8 · outbound

This paper cites Characterizing memorization in diffusion language models: Generalized extraction and sampling effects.arXiv preprint arXiv:2603.02333, 2026.

Extracting Training Data from Diffusion Language Models via Infilling Characterizing memorization in diffusion language models: Generalized extraction and sampling effects.arXiv preprint arXiv:2603.02333, 2026

Reference 31

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Observation 400f40d7-2b5f-4044-bcbf-2c131d55862e · outbound

This paper cites Microsoft Presidio: Context aware, pluggable and customizable PII anonymization service for text and images.https://microsoft.github.io/presidio, 2018.

Extracting Training Data from Diffusion Language Models via Infilling Microsoft Presidio: Context aware, pluggable and customizable PII anonymization service for text and images.https://microsoft.github.io/presidio, 2018

Reference 32

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Observation 50aa581b-23f9-49f7-8cb2-b7cd5fff00f8 · outbound

This paper cites Feder Cooper, Daphne Ippolito, Christopher A.

Extracting Training Data from Diffusion Language Models via Infilling Feder Cooper, Daphne Ippolito, Christopher A

Reference 33

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Observation 68fc55f6-5ff0-4f6f-a7fe-02e06b1aab05 · outbound

This paper cites Large language diffusion models.

Extracting Training Data from Diffusion Language Models via Infilling Large language diffusion models

Reference 34

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This paper cites Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (hipaa) privacy rule.

Extracting Training Data from Diffusion Language Models via Infilling Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (hipaa) privacy rule

Reference 35

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Observation fec5f748-08be-414f-8495-b637548c0c54 · outbound

This paper cites Dynhd: Hallucination detection for diffusion large language models via denoising dynam- ics deviation learning.arXiv preprint arXiv:2603.16459.

Extracting Training Data from Diffusion Language Models via Infilling Dynhd: Hallucination detection for diffusion large language models via denoising dynam- ics deviation learning.arXiv preprint arXiv:2603.16459

Reference 36

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Observation 84f8d8a4-8036-4094-8e58-b63f9970765f · outbound

This paper cites Balancing innovation and oversight: Ai in the us treasury and irs: A survey.

Extracting Training Data from Diffusion Language Models via Infilling Balancing innovation and oversight: Ai in the us treasury and irs: A survey

Reference 37

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Observation add69936-4d89-431f-9b11-6f88c4865e30 · outbound

This paper cites Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference.

Extracting Training Data from Diffusion Language Models via Infilling Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference

Reference 38

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Observation 7804315e-264b-4138-a62a-6ac95119267d · outbound

This paper cites Self-conditioned Embedding Diffusion for Text Generation.

Extracting Training Data from Diffusion Language Models via Infilling Self-conditioned Embedding Diffusion for Text Generation

Reference 39

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Observation af87db23-644a-4e46-b886-3f735387d976 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Extracting Training Data from Diffusion Language Models via Infilling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 40

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Observation 9a1b4723-6c40-4a1a-8d1f-3d9dbfb3361b · outbound

This paper cites Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning.

Extracting Training Data from Diffusion Language Models via Infilling Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning

Reference 41

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This paper cites Department of the Treasury.

Extracting Training Data from Diffusion Language Models via Infilling Department of the Treasury

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:25:03.588414Z

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Observation 87e750e1-f49f-4072-bbd4-c55d777accde · outbound

This paper cites The devil behind the mask: An emergent safety vulnerability of diffusion LLMs.

Extracting Training Data from Diffusion Language Models via Infilling The devil behind the mask: An emergent safety vulnerability of diffusion LLMs

Reference 43

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Observation 960fe4ce-ec65-41e4-ac29-dabd8cda3afb · outbound

This paper cites Fast-dLLM: Training-free acceleration of diffusion LLM by enabling KV cache and parallel decoding.

Extracting Training Data from Diffusion Language Models via Infilling Fast-dLLM: Training-free acceleration of diffusion LLM by enabling KV cache and parallel decoding

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:05:04.293433Z

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Observation 8b770911-4113-44ae-b2cb-7afffd07e975 · outbound

This paper cites The landscape of memorization in.

Extracting Training Data from Diffusion Language Models via Infilling The landscape of memorization in

Reference 45

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Observation 9b01e499-ebd1-48bd-af65-10293ab3ad28 · outbound

This paper cites Energy-based diffusion language models for text generation.

Extracting Training Data from Diffusion Language Models via Infilling Energy-based diffusion language models for text generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:25:03.602073Z

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Observation 89c46674-8bb7-4867-8788-b61c648998bc · outbound

This paper cites Dream 7B: Diffusion Large Language Models.

Extracting Training Data from Diffusion Language Models via Infilling Dream 7B: Diffusion Large Language Models

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-06-30T16:04:53.260636Z

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Observation e9389dc7-8bb6-4b67-bac3-a2495b978c97 · outbound

This paper cites Bag of tricks for training data extraction from language models.

Extracting Training Data from Diffusion Language Models via Infilling Bag of tricks for training data extraction from language models

Reference 48

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Observation 39e2909f-1efb-4d33-a667-4c49b5432b75 · outbound

This paper cites Exploring memorization in fine-tuned language models.

Extracting Training Data from Diffusion Language Models via Infilling Exploring memorization in fine-tuned language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:25:03.596968Z

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Observation d1e7f9e0-7d38-451d-8ba5-7369e13e9180 · outbound

This paper cites Jailbreaking large language diffusion models: Revealing hidden safety flaws in diffusion-based text generation.

Extracting Training Data from Diffusion Language Models via Infilling Jailbreaking large language diffusion models: Revealing hidden safety flaws in diffusion-based text generation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:25:03.592258Z

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Observation b74c913e-e324-49e2-891b-9dac8ae6c4eb · outbound

This paper cites an unresolved cited work.

Extracting Training Data from Diffusion Language Models via Infilling Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-07-08T15:25:03.632787Z

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Observation a156beb8-21b3-4810-88f6-66a5e2ab2ef2 · outbound

This paper cites d1: Scaling reasoning in diffusion large language models via reinforcement learning.

Extracting Training Data from Diffusion Language Models via Infilling d1: Scaling reasoning in diffusion large language models via reinforcement learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T15:05:04.291200Z

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Observation 2aeb7e55-529c-4118-84bc-27d5ff5c6004 · outbound

This paper cites dllm: Simple diffusion language modeling.arXiv preprint arXiv:2602.22661.

Extracting Training Data from Diffusion Language Models via Infilling dllm: Simple diffusion language modeling.arXiv preprint arXiv:2602.22661

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:04:53.265351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers