Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.00611.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T13:03:48.473546Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T12:07:50.586979Z
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 de88a93a-729b-442c-a160-07813ee754b3 · inbound
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation cites this paper.
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:03:48.473546Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa5be899-57cd-4e43-8d9b-b25da2b19236 · 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 Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:06:23.659079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 737cba04-5ba5-4259-b370-88d56e8c0dfe · 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 Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T12:07:50.589159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.