Paper Citation Record · LEDGER

How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2111.09509.

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

pith.paper-citation-record.v1
2111.09509 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:46:24.009199Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6b117ca6-a510-45de-b875-cfc3255b08fd · inbound

Quantifying Memorization Across Neural Language Models cites this paper.

Quantifying Memorization Across Neural Language Models How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T22:04:59.739743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 241e5333-fab6-4918-9704-aacbedce2eca · inbound

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment cites this paper.

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 192

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:30:45.015799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf2ddbc4-6f74-4186-baf4-71a210d9bc7b · inbound

The Rise and Potential of Large Language Model Based Agents: A Survey cites this paper.

The Rise and Potential of Large Language Model Based Agents: A Survey How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 217

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:47:47.946987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation daad3906-b4c3-47b2-8236-fcf1a831ea03 · inbound

On the Privacy Risk of In-context Learning cites this paper.

On the Privacy Risk of In-context Learning How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T19:46:24.009199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63e95b54-9872-40b7-9c53-d968867ab606 · inbound

Data Compressibility Quantifies LLM Memorization cites this paper.

Data Compressibility Quantifies LLM Memorization How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:02:07.687992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 761b1ff1-1401-41db-a09b-aeb2f2d888b8 · inbound

When transformers learn "impossible" languages, what do they learn? cites this paper.

When transformers learn "impossible" languages, what do they learn? How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T02:15:14.188523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.