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Paper Citation Record · LEDGER

Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2403.16248.

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

pith.paper-citation-record.v1
2403.16248 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:39:45.994728Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T10:33:18.386577Z

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 ccc67718-842c-47d8-b5cf-01132eb1e5df · inbound

Concept Navigation and Classification via Open-Source Large Language Model Processing cites this paper.

Concept Navigation and Classification via Open-Source Large Language Model Processing Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-08T21:39:45.994728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:39:45.994728Z digest=sha256:e6fd5ebf6ca537d166849d4eb41a6e91c3ea6e7d033d0e064a483f4a1b615601

Observation 85321a6d-80cc-4e59-b49d-c453de800edf · inbound

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows cites this paper.

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T23:25:14.889499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:25:14.889499Z digest=sha256:67835c13ba5e72b734e0224f1f8faccbd8a1a307230f2693fbb702043494ce29

Observation 44a02d49-ebab-4af1-9a4b-6f297898b859 · inbound

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry cites this paper.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T10:06:23.655340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:06:23.655340Z digest=sha256:e3e165f407d9d89eba1fd950a3f61b815a38ee35b38127dbe647d28525f4a0f2

Observation fc02d921-93e9-417d-8d65-1d4e02f69017 · inbound

Investigating Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs cites this paper.

Investigating Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-16T12:07:50.594554Z

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=2026-05-16T12:06:14.298660Z digest=sha256:3d17353e7f34bcda92a7acd2cd576ffb76b64d19067ffd086243c5b4b4c1c86b

Observation 6e0abb44-c04d-48d1-b5a4-5cfb1bb15554 · inbound

Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest cites this paper.

Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:41:01.758837Z

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=2026-05-10T03:20:12.777878Z digest=sha256:6684373677484518a9f1beef906f9dca5d3c8f2fadace8a46f6f6cfa93bfbb29

Observation 2d903c36-398a-47a5-98da-43adbe011378 · inbound

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges cites this paper.

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-29T10:33:18.388259Z

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=2026-06-29T10:32:47.756343Z digest=sha256:430a83f25c161758ea1debbf77b7dc7bff0199725334b01749a1c648f4053984

Observation 302aaa55-7b18-4504-af34-a9b27ff400ad · inbound

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities cites this paper.

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-11T23:15:06.472205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T23:15:06.472205Z digest=sha256:d4417383a4dca8512e841eb008520404c7d6d9022e0e73d2ceeca25a62ccd6ee