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Paper Citation Record · LEDGER

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2508.14062.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.14062 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:16:20.652967Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 221b97f3-82f7-4f9c-a5b2-7e3d1ba2fec2 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:16:20.858819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.581293Z digest=sha256:e07e6809ee6e12ecc3e55de630897139b0de31d56ca3e212db9df2e45aedd593

Observation 256eb1f4-f9a9-4682-99a1-57f2da817d7f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.584508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.584508Z digest=sha256:45c6d65f11249e26349378567baeabbf9202f1126c91aa728b3c39db0cd9fc93

Observation 93b3ef15-adb6-4b76-bc93-16db59e74c5a · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.850940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.588412Z digest=sha256:e03e28ebe9b179c8a19f01f289e7633874ed08d60e48349638be3c7dea182808

Observation f822d152-58df-4e8f-8dc4-0f7a79a19080 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.842809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.591462Z digest=sha256:ac6204a345fb7fbcf9e47a7a3d6aef93c487454ea5a44e6130ce828aa7ddc539

Observation 4b6e5aab-38d5-4b80-8fe6-9120f0a38be7 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.835216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.594461Z digest=sha256:7379d10ee80674356f19fa2cda44b39f839f8dcc24bbea3da089674382185330

Observation 1b561331-7d16-450d-94be-2deb40f756d2 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.597382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.597382Z digest=sha256:b1562924c32f587eeda67bab59b9d12ddd4836bddb6d9586fb83ab2db8c7d5b9

Observation 299a7b00-9f2f-4d15-a239-fc6a93ca9778 · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models B., Mironov, I., Talwar, K., and Zhang, L

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.600551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.600551Z digest=sha256:8b93221cbd2b0b0000eea406b00e336c1f2b2a768485a4ce463295fde5d0f588

Observation 974f5df4-780e-47bb-b943-4b4eac99df70 · outbound

This paper cites A., Kamath, G., Kulkarni, J., Lee, Y.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models A., Kamath, G., Kulkarni, J., Lee, Y

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:16:20.812213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.603691Z digest=sha256:f6b5d57a45acdb421305b4eead951cdc40d03df83e66bc90d4737ecc9f621886

Observation 1de3e908-775a-466b-936f-895cde76e91c · outbound

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

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Scalable Extraction of Training Data from (Production) Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.610053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.610053Z digest=sha256:c28bf66a3f9bdb20a2a4c2b6181054d9edf6b3e8df1ba59938e3a56fa026a605

Observation 2478f5c8-f823-43d8-a942-fa2789db7faf · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.801086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.615885Z digest=sha256:8d2dc4115bb5778e888c3122e5a24cb99fae06e46bb9ba1306c7978c952870a8

Observation 4f0ed442-6935-450a-a8f5-6898d5793ee7 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.791892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.619725Z digest=sha256:16531e82a53214881d6eadce4e4d88232dea725551d56f078b00cc50f9f5b641

Observation 1d65d8db-c5a9-4d91-b00b-9215069098c4 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.780093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.623101Z digest=sha256:8595239438c41d921e5de2bb3bc037fbccec5cc297aebf60de872f5b2fd70d57

Observation ec4ccd34-7cf0-4cdb-a7f9-c50b9c1dbfd7 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.769324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.626526Z digest=sha256:9406673d1d8876eb488574250966e4d98bd893d02afcedf396ef729704965374

Observation b4f9c85e-a6bf-4f33-beb8-9d5ea52c5544 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.758379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.629971Z digest=sha256:e9db3f83884953391e3fc614a41e4841b37e8102e2b4eabb0a5dee75b58685d8

Observation dede76d7-26b6-4159-91ab-c86865eb0284 · outbound

This paper cites Dataset Inference: Ownership Resolution in Machine Learning.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Dataset Inference: Ownership Resolution in Machine Learning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.633994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.633994Z digest=sha256:2201538392bc63d2c61d775858409be930fa0be16919ba88657e58206605cc32

Observation 9c55b956-390d-40fe-bfc6-a8b933068864 · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.749042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.637731Z digest=sha256:2728271654e21560ec8dc7679cff94f776ced6e319e506aae7c5bdfa0ba7d126

Observation 2eec25ce-e042-4340-838a-1176419132d4 · outbound

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

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.640973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.640973Z digest=sha256:ae719de13d17fb7f788cd860f35af2d7b9b58edc50af2487d06130ba8b3ed851

Observation 4eb36ad8-2bb4-4799-b9ac-72fb14290274 · outbound

This paper cites S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:16:20.738329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.645117Z digest=sha256:ad2aeb3ef9b0538505942df307aead145c34f2c7e99e937449a5fa870bb1b97d

Observation 8fe89b00-3c66-4e2e-acbc-25926faf461b · outbound

This paper cites an unresolved cited work.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:16:20.727810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-05T22:16:20.649403Z digest=sha256:4cc07838b8159c1939d31094cb4173d2bdb621d177e8b9326031a4607cef98d0

Observation a93417a2-cfe6-4ee8-8e5a-bd123f357c41 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models On the Opportunities and Risks of Foundation Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-05T22:16:20.652967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:16:20.652967Z digest=sha256:53880578ea6b2fa78376dc23a2a8307134df101376a71a278568c6ab5bfa5826

Pith citing papers

No inbound Pith citation observations are available.