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Paper Citation Record · LEDGER

Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.17753.

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

pith.paper-citation-record.v1
2406.17753 v3

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:41:06.392684Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:53:19.172975Z

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 816f60f6-bbef-43dc-9638-ad1597c3485a · inbound

Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications cites this paper.

Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:53:19.176172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T17:49:21.098232Z digest=sha256:b814c2c09bb87cfe5b5743de1f37ea28cf4e7aa0c5decba209f14f50e5782ea7

Observation 4d75ae92-2f73-4483-be96-6d3808f1d62f · inbound

Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models cites this paper.

Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T21:41:06.392684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:41:06.392684Z digest=sha256:8eadaba64a541668f9577c9fa92c4211eddd4ba09b160dd5e8765ae06e92e01d

Observation 319f121d-f7be-4b3d-bb7c-32c8f7e5aaca · 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 Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T05:48:18.736717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:48:18.736717Z digest=sha256:1a286e97a656ac1fe7e72c098e2bfaedff769e7bb57a45a6a35cc29c7c9b01f2

Observation 4daf4eb6-48d7-4ec4-8c8b-398f8a9797ba · inbound

Tailored untruths: How personalisation challenges LLM safeguards cites this paper.

Tailored untruths: How personalisation challenges LLM safeguards Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-04T09:55:53.526794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T09:55:53.526794Z digest=sha256:1837a600e6bee478d7ef7514d40b663b689e180ff5b745c8e6768967ea5413f5

Observation 2b88576f-89c4-48ea-957b-b612d298f715 · inbound

It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation cites this paper.

It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-01T17:30:01.143794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T17:30:01.143794Z digest=sha256:eb099f1c44d0252d4720a2f7b5793b8d6b735a152c2798143023bd47eb6d5539