Paper Citation Record · LEDGER

Factuality of Large Language Models: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.02420.

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

pith.paper-citation-record.v1
2402.02420 v3

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:11:36.169797Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:37:22.698075Z

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 bbc393ba-c476-48b7-8768-3102e390ddd0 · inbound

Eliciting Language Model Behaviors with Investigator Agents cites this paper.

Eliciting Language Model Behaviors with Investigator Agents Factuality of Large Language Models: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T16:11:36.169797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f65fc0bd-1523-4229-a7ee-f6471302f18a · inbound

AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models cites this paper.

AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models Factuality of Large Language Models: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:18.746771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b70240b4-26e7-4d43-8b51-b747422c83f2 · inbound

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning cites this paper.

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning Factuality of Large Language Models: A Survey

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:32.997345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55d6146-7fde-416b-b4f3-b006f12a3f5e · inbound

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework cites this paper.

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework Factuality of Large Language Models: A Survey

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:36:15.344297Z

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.

Observation b09d09c4-3956-477e-9ba1-a1c4f865550a · inbound

Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks cites this paper.

Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks Factuality of Large Language Models: A Survey

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-21T11:10:01.987837Z

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.

Observation 81e58a6a-c422-446a-95c5-9829181198df · inbound

Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation cites this paper.

Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation Factuality of Large Language Models: A Survey

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:37:22.699420Z

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.