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Paper Citation Record · LEDGER

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

As of 10 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:f96da1cd06a663eb4e044e075a5681e56e90bbd3f94a6f0ef0cd24336e45afd8

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=sha256:31b6b862f3d266c7cff925e361c341e979a692761d1f4f0a8e2433b47460a94f

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=sha256:3d956ee52245cdc9b43598070dd28c185b8ee6b53d13cbba504416963ab5019f

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=sha256:766c58b88249c7d5b407790424d556d13cfd34c66434d173d8d28b7fd4f2660b

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=sha256:7a2168d7c8b9aa6393c52e85b671e9138c75508e0340f8099a539f2c66159fe4

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=sha256:37d2f818199b7ed8cdef7c5db126fe250fa238dc0ac5222c7a65880c94317372

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=sha256:47e2052183cf30613d4c728a4586ce6ee2f0d5f7e9c649bda85055589bf681f5