Paper Citation Record · LEDGER

Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2404.09329.

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

pith.paper-citation-record.v1
2404.09329 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 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 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:57:41.333346Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:16:47.581665Z

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 7eac6596-299c-4c4c-8f4f-4aa66377104d · 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 Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T17:53:19.165453Z

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.

Observation 0e16e85f-d134-492f-be88-8187973673fa · inbound

Verbalized Bayesian Persuasion cites this paper.

Verbalized Bayesian Persuasion Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T14:57:41.333346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c23ebf-c707-46ad-a78e-1d0ebf328653 · 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 Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T21:41:06.309498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14d30ca4-b485-4741-b455-f2bd877ff7ed · inbound

Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles cites this paper.

Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:11:23.778139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eff5885-cb40-4d15-9017-88cde5f59d8b · inbound

Does AI and Human Advice Mitigate Punishment for Selfish Behavior? An Experiment on AI ethics From a Psychological Perspective cites this paper.

Does AI and Human Advice Mitigate Punishment for Selfish Behavior? An Experiment on AI ethics From a Psychological Perspective Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 443

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:02.761354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68905ea6-4f66-44a8-8f7c-2a75398ca2f5 · inbound

Prosocial Persuasion at Scale? Large Language Models Outperform Humans in Donation Appeals Across Levels of Personalization cites this paper.

Prosocial Persuasion at Scale? Large Language Models Outperform Humans in Donation Appeals Across Levels of Personalization Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 6037

Resolution: unresolved; no resolver link, observed 2026-08-02T16:53:22.625551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed404249-8ee8-45f0-ae1d-cb8552862c38 · inbound

Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations cites this paper.

Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:00:50.176825Z

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.

Observation bacd05ea-d313-4394-800c-59b8ab66cbeb · inbound

LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies cites this paper.

LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:18:32.641397Z

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.

Observation 76f77134-b1b7-4ea5-967c-1b3d635cd22f · inbound

Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations cites this paper.

Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:16:08.030403Z

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.

Observation d73bca64-c4dd-41e7-926b-2666a2c34b4d · inbound

Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools cites this paper.

Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:25.539272Z

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.

Observation 476f4af1-8c9f-425b-8fd6-59bc02fa5386 · inbound

Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools cites this paper.

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:15:43.759452Z

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.

Observation 2342644c-2255-4e10-98a5-7f9bf56d5e88 · inbound

LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight cites this paper.

LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:41:24.548777Z

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.

Observation 2a69e9da-7b6e-4459-9dfd-ca0f2c3d05c0 · inbound

When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance cites this paper.

When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:50:23.458448Z

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.

Observation c72723b6-ae33-4ab6-a1dc-9848f486f0b1 · inbound

Human Decision-Making with Persuasive and Narrative LLM Explanations cites this paper.

Human Decision-Making with Persuasive and Narrative LLM Explanations Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:06:34.078602Z

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.

Observation a4b4f890-9ccf-4df2-a747-12ce0b288f9b · inbound

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing cites this paper.

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:16:47.583304Z

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.