What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests | Paper Citation Record

Observation c6449746-ec07-4e44-9261-9ac22aa358ae · outbound

This paper cites Advances in Neural Information Processing Systems 36, 66044–66063 (2023).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems 36, 66044–66063 (2023)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.803005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 617dfb47-6cb7-45cf-9100-11fc9d234fd9 · outbound

This paper cites In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.517054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1668b07d-6a44-4153-b222-eaeb43072d53 · outbound

This paper cites Advances in Neural Information Processing Systems36, 28072–28090 (2023).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems36, 28072–28090 (2023)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.791515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d4386650-4a34-4d21-b632-3a5422656052 · outbound

This paper cites In: International Conference on Machine Learning.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: International Conference on Machine Learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.779703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6168c408-2a48-4f4a-833b-0db3caf7f57b · outbound

This paper cites Artificial Intelligence Review58(3), 90 (Jan 2025).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Artificial Intelligence Review58(3), 90 (Jan 2025)

Reference 5

Resolution: verified exact; doi, observed 2026-08-06T17:21:33.880369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e88d542f-cb6b-49b2-bdaa-bf0031aac86a · outbound

This paper cites In: 2021 IEEE symposium on security and privacy (SP).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: 2021 IEEE symposium on security and privacy (SP)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.535484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daddd18c-f82b-4192-a569-dd84f3c19d79 · outbound

This paper cites Computer networks and ISDN systems30(1-7), 107–117 (1998).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Computer networks and ISDN systems30(1-7), 107–117 (1998)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.540121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30bda9ed-08f0-419f-bf52-3f5f8624fb9f · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2022).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: The Eleventh International Conference on Learning Representations (2022)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.757246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d2950a7-fcbb-4fc9-b242-0d2c40ba6936 · outbound

This paper cites In: 28th USENIX security symposium (USENIX security 19).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: 28th USENIX security symposium (USENIX security 19)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.746939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b0a2967-6868-4495-8b81-46222b80ce65 · outbound

This paper cites Stealing Part of a Production Language Model.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Stealing Part of a Production Language Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.552558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c9831b1-7978-4619-b54d-760d1267ebdf · outbound

This paper cites In: 30th USENIX security symposium (USENIX Security 21).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: 30th USENIX security symposium (USENIX Security 21)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.736260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a21393ef-e8df-491b-acdf-ae8db5cdb58d · outbound

This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.726066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5ecfb068-77f9-47b6-8a0f-161726abcc6b · outbound

This paper cites Transactions of the Association for Computational Linguistics 12, 283–298 (2024).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Transactions of the Association for Computational Linguistics 12, 283–298 (2024)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.715510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8add6027-20fe-4941-adcf-8e0ad70d6728 · outbound

This paper cites Knowledge Neurons in Pretrained Transformers.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Knowledge Neurons in Pretrained Transformers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.568852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef5f496-2eed-494f-9128-b865bde26af8 · outbound

This paper cites Do Membership Inference Attacks Work on Large Language Models?.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Do Membership Inference Attacks Work on Large Language Models?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.573107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12ccf385-c969-4337-b35c-c33e0a977a26 · outbound

This paper cites Who's Harry Potter? Approximate Unlearning in LLMs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Who's Harry Potter? Approximate Unlearning in LLMs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.578374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fffbb034-1015-4032-8fce-e3024fdd6ea3 · outbound

This paper cites an unresolved cited work.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:21:34.705849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4562dc1d-1869-48be-ad85-1c5d0cb7803d · outbound

This paper cites Making Pre-trained Language Models Better Few-shot Learners.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Making Pre-trained Language Models Better Few-shot Learners

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.586718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed263b22-1b1b-4731-a014-11666c8fd741 · outbound

This paper cites Advances in neural information processing systems32 (2019).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in neural information processing systems32 (2019)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.695218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 615dbb3a-9e5a-40f1-845d-90e8d081e07c · outbound

This paper cites The Llama 3 Herd of Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests The Llama 3 Herd of Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.596091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30d44810-ff1d-44d1-806b-63460cfb3d3c · outbound

This paper cites Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.601117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c8ea816-aca0-40e8-b4a4-1818ab5684c7 · outbound

This paper cites In: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.685276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4a339cfc-d6af-4696-a00c-2718f9d8480d · outbound

This paper cites Are Large Pre-Trained Language Models Leaking Your Personal Information?.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Are Large Pre-Trained Language Models Leaking Your Personal Information?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.610605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 709823ba-4894-47d1-9be4-3799af0008d8 · outbound

This paper cites Editing Models with Task Arithmetic.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Editing Models with Task Arithmetic

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.614861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6972d0e2-ce1e-4b72-b6ee-619347d51da0 · outbound

This paper cites Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.619541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7f6211-ac17-44a8-a2a4-8be6a0dcb4d7 · outbound

This paper cites RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.624575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77e46d45-8a45-4a80-abb6-3517ca1a18db · outbound

This paper cites Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.629117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c318e099-d703-4b7c-8d20-75d33d4c0b38 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.633131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 054be028-38d2-44f6-a027-71e9a99fbf1f · outbound

This paper cites Advances in Neural Information Processing Systems 36, 20750–20762 (2023).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems 36, 20750–20762 (2023)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.674478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3878edba-d90a-4ec9-ad9e-e11c8324e386 · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.664155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bdf47fc5-d7e4-4472-927e-9da755c77e87 · outbound

This paper cites Large Language Models Can Be Strong Differentially Private Learners.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Large Language Models Can Be Strong Differentially Private Learners

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.646127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d66d80e-401a-4486-86ec-5baa872c2de5 · outbound

This paper cites Advances in Neural Information Processing Systems 37, 118198–118266 (2024).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems 37, 118198–118266 (2024)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.653216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d911cbf0-64e7-4088-87b8-f12be1720d27 · outbound

This paper cites Rethinking Machine Unlearning for Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Rethinking Machine Unlearning for Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.653771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd9512fd-39da-487f-a715-3dac4aee5c56 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.643525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 44a92604-5952-43f8-9f37-f07e9ace23df · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.662178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6a0557-b99b-4b6c-8d9f-c7d264df0dfe · outbound

This paper cites An Adversarial Perspective on Machine Unlearning for AI Safety.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests An Adversarial Perspective on Machine Unlearning for AI Safety

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.666080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf415a3a-3e94-472a-923f-1ea5981a13af · outbound

This paper cites Eight Methods to Evaluate Robust Unlearning in LLMs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Eight Methods to Evaluate Robust Unlearning in LLMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.670555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64a9a68-e94d-44ed-bf94-f18e71f9a01c · outbound

This paper cites TOFU: A Task of Fictitious Unlearning for LLMs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests TOFU: A Task of Fictitious Unlearning for LLMs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.676102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b35f92b2-eb7c-4919-951b-578439f330bb · outbound

This paper cites In: 33rd USENIX Security Symposium (USENIX Security 24).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: 33rd USENIX Security Symposium (USENIX Security 24)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.632233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 02ba38df-1de7-477c-9eb1-7c56d5625101 · outbound

This paper cites Advances in neural information processing systems35, 17359– 17372 (2022).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in neural information processing systems35, 17359– 17372 (2022)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.621930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc66222a-e0ec-4d50-ae97-cab50d9f8479 · outbound

This paper cites Language Model Inversion.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Language Model Inversion

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.687986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bc25c06-293f-4ba8-829b-4cacc61c4e7f · outbound

This paper cites arXiv:2402.00751 (2024).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests arXiv:2402.00751 (2024)

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.692645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa3d42ea-d396-44f6-ba71-df2f8a2c2361 · outbound

This paper cites PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:21:34.197340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2afd143c-25c0-4451-be86-260ce2ea6f00 · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Scalable Extraction of Training Data from (Production) Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.701131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9128333-475b-44e2-8e06-7ba4ce92964c · outbound

This paper cites A Survey of Machine Unlearning.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests A Survey of Machine Unlearning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.704900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bba9440c-b6a5-499c-ac09-bc6466054caa · outbound

This paper cites https://noyb.eu/en/ chatgpt-provides-false-information-about-people-and-openai-cant-correct-it (2024), accessed: 2025-06-02.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests https://noyb.eu/en/ chatgpt-provides-false-information-about-people-and-openai-cant-correct-it (2024), accessed: 2025-06-02

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.611778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f79e12c-0699-4928-a6e9-b7a6f558e5b8 · outbound

This paper cites an unresolved cited work.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.713250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed82dce-814b-4478-b7ee-082a859ac8ec · outbound

This paper cites Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.717513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c736dc57-e7c8-4c9e-be0a-e5f44ac5adf3 · outbound

This paper cites In-Context Unlearning: Language Models as Few Shot Unlearners.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.721715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b5fe0d9-7c6a-4493-b388-83f1d6223531 · outbound

This paper cites Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.726083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 834c7d1d-b229-4ed8-a94e-a4f94d67b36a · outbound

This paper cites Targeting the Benchmark: On Methodology in Current Natural Language Processing Research.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Targeting the Benchmark: On Methodology in Current Natural Language Processing Research

Reference 51

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:21:34.128255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d0821fda-3e3e-4dad-9962-6254eec3ebd8 · outbound

This paper cites In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.599429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 14458d71-94a2-4609-b6bb-885f9b84a710 · outbound

This paper cites arXiv:2505.17117 (2025).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests arXiv:2505.17117 (2025)

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.739325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9fd1ad1-7c18-4a68-a15b-1f7a162f0922 · outbound

This paper cites Detecting Pretraining Data from Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Detecting Pretraining Data from Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.744409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fdc78ac-3792-4101-a152-8f2dedf7a926 · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.749354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 631b130a-e5ae-45b6-82a5-eda49b418f47 · outbound

This paper cites Beyond Memorization: Violating Privacy Via Inference with Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Beyond Memorization: Violating Privacy Via Inference with Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.754016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d8ba1b9-a678-4802-9b12-bcf44bc55902 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.758073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8625c895-970f-43fb-b720-154ba2a8ae4f · outbound

This paper cites Guardrail Baselines for Unlearning in LLMs.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Guardrail Baselines for Unlearning in LLMs

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.762101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bca4c09-851c-4338-ba73-ae22ed7760f4 · outbound

This paper cites Sequence-Level Leakage Risk of Training Data in Large Language Models.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Sequence-Level Leakage Risk of Training Data in Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.766535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e0f27bb-15a0-416f-a261-7c2b85f5670f · outbound

This paper cites In: Belkin, M., Kpotufe, S.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Belkin, M., Kpotufe, S

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.588746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7d000c9a-2972-4512-882f-fdf2289733e0 · outbound

This paper cites On Uncertainty In Natural Language Processing.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests On Uncertainty In Natural Language Processing

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:21:33.896132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a08fad3b-9239-48c5-bbe7-c4e72c86ead9 · outbound

This paper cites (De)-Indexing and the Right to be Forgotten.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests (De)-Indexing and the Right to be Forgotten

Reference 62

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:21:33.850769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d237a434-3a0b-4e68-a530-43fa002e0a28 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.577469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 81c933b8-809a-42d4-b1b2-3555c4195c03 · outbound

This paper cites In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.567174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e30b72f7-b1bb-4679-af7c-b7655f697fed · outbound

This paper cites Advances in Neural Information Processing Systems37, 105425–105475 (2024).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems37, 105425–105475 (2024)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.556334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9eda09fe-6ff1-4484-9223-558e38a45fe2 · outbound

This paper cites Advances in Neural Information Processing Systems 36, 39321–39362 (2023).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Advances in Neural Information Processing Systems 36, 39321–39362 (2023)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.544494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c80b3b9-52a8-4530-8684-9b6f0bc2c90d · outbound

This paper cites AI and Ethics (Sep 2024).

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests AI and Ethics (Sep 2024)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:34.533212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb624501-695f-4efd-94bc-f0c0bc477508 · outbound

This paper cites an unresolved cited work.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Unresolved cited work

Reference 68

Resolution: verified exact; doi, observed 2026-08-06T17:21:33.834244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers