Pith. sign in

Paper Citation Record · LEDGER

Under the Surface: Tracking the Artifactuality of LLM-Generated Data

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.14698.

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

pith.paper-citation-record.v1
2401.14698 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:43:55.447367Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T21:33:28.546653Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 992c5b94-0ba4-444f-84f6-cdb6f9393361 · inbound

MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models cites this paper.

MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:33:28.550360Z

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=pdf_text observed=2026-05-23T21:31:27.597981Z digest=sha256:c2372378a3edc2fe1f78fa0cf665513795d0c16e37c3ab817b5c7437421f0036

Observation bbeb2dec-d8ff-4b99-a599-3c8339f61c10 · inbound

Bias in Large Language Models: Origin, Evaluation, and Mitigation cites this paper.

Bias in Large Language Models: Origin, Evaluation, and Mitigation Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-05-23T17:08:12.447474Z

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=pdf_text observed=2026-05-23T17:08:09.267577Z digest=sha256:a1205150d550b30707bdcaa72e83a1fa66893edf5ad39c1126f2725a0315c355

Observation dd518585-029f-45e5-966c-6fe808d88d14 · inbound

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications cites this paper.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-09T17:43:55.447367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:43:55.447367Z digest=sha256:42160886620d5c296b45847f66542efdfae0c5da84ca4eb2647506d73cb4289f

Observation 5656937d-883b-42bd-a7ed-a8fbd480a183 · inbound

Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts cites this paper.

Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T22:15:03.169756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:15:03.169756Z digest=sha256:1beed413548283826b96d72409ebc75df81b7d57051682df72915814870304ab

Observation d8795d3f-9d82-4a8d-81e3-5bc59ea6db0f · inbound

BharatBBQ: A Multilingual Bias Benchmark for Question Answering in the Indian Context cites this paper.

BharatBBQ: A Multilingual Bias Benchmark for Question Answering in the Indian Context Under the Surface: Tracking the Artifactuality of LLM-Generated Data

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T22:23:09.575651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:23:09.575651Z digest=sha256:cbe52709053673fe98d8815d0ba2625b527d1af6dfa4bda775d1c6822ab286eb