Paper Citation Record · LEDGER

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics

As of 22 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 4 inbound Pith citation observations for arXiv:2501.17273.

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

pith.paper-citation-record.v1
2501.17273 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:45:43.198440Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:35:39.906365Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:16:47.516630Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 784f8a8c-415f-40a9-8d6b-ee80ab5c326e · outbound

This paper cites What is israeli firm stoic and how it tried to disrupt lok sabha polls 2024.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics What is israeli firm stoic and how it tried to disrupt lok sabha polls 2024

Reference 1

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Observation 80ffd939-4a0a-463c-a143-bdffd2a5ad31 · outbound

This paper cites Challenges in Red Teaming AI Systems, June.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Challenges in Red Teaming AI Systems, June

Reference 2

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This paper cites C., Elson, M., and Schneider, I.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics C., Elson, M., and Schneider, I

Reference 3

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Observation 3cfe5918-b33f-419e-b145-c238180c115c · outbound

This paper cites The dark side of language models: Exploring the potential of llms in multimedia disinformation generation and dissemination.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics The dark side of language models: Exploring the potential of llms in multimedia disinformation generation and dissemination

Reference 4

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Unresolved cited work

Reference 5

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Observation d49b9959-13c8-49b3-9578-87b1c42ed328 · outbound

This paper cites The Persuasive Power of Large Language Models.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics The Persuasive Power of Large Language Models

Reference 6

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This paper cites Truth, Lies, and Automation How Language Models Could Change Disinformation.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Truth, Lies, and Automation How Language Models Could Change Disinformation

Reference 7

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This paper cites Debate format: Three rounds of structured debate, 2020.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Debate format: Three rounds of structured debate, 2020

Reference 8

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This paper cites Non-determinism in gpt-4, 2023.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Non-determinism in gpt-4, 2023

Reference 9

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This paper cites Committee on national security systems (cnss) glos- sary.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Committee on national security systems (cnss) glos- sary

Reference 10

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Observation 18189b4c-015c-4446-8cbb-45d1ecbc3cc1 · outbound

This paper cites Durably reducing conspiracy beliefs through dialogues with ai.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Durably reducing conspiracy beliefs through dialogues with ai

Reference 11

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This paper cites A Guide to Min- imum Wage in India, September 2024.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics A Guide to Min- imum Wage in India, September 2024

Reference 12

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This paper cites The Llama 3 Herd of Models.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics The Llama 3 Herd of Models

Reference 13

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Measuring the persuasiveness of language models

Reference 14

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This paper cites A day in the life of a Shwe Kokko scammer, June.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics A day in the life of a Shwe Kokko scammer, June

Reference 15

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This paper cites A Survey on Offensive AI Within Cybersecurity.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics A Survey on Offensive AI Within Cybersecurity

Reference 16

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Observation eccf3b9b-abce-4a39-be34-dfa5a410dc8b · outbound

This paper cites Predicting personality with social media.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Predicting personality with social media

Reference 17

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This paper cites Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations

Reference 18

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This paper cites D., Rentfrow, P.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics D., Rentfrow, P

Reference 19

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This paper cites Why report estimated marginal means?, aug 2021.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Why report estimated marginal means?, aug 2021

Reference 20

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This paper cites and Margetts, H.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics and Margetts, H

Reference 21

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This paper cites A russian bot farm used ai to lie to americans.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics A russian bot farm used ai to lie to americans

Reference 22

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This paper cites Bots and misinformation spread on social media: Implications for covid-19.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Bots and misinformation spread on social media: Implications for covid-19

Reference 23

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This paper cites Of 2 minds: How fast and slow thinking shape perception and choice [excerpt].

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Of 2 minds: How fast and slow thinking shape perception and choice [excerpt]

Reference 24

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This paper cites Private traits and attributes are predictable from digital records of human behavior.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Private traits and attributes are predictable from digital records of human behavior

Reference 25

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics ReMoDetect: Reward Models Recognize Aligned LLM's Generations

Reference 26

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics and Warren, P

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Lost in the Middle: How Language Models Use Long Contexts

Reference 28

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model

Reference 29

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Studying User Footprints in Different Online Social Networks

Reference 30

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics C., Teeny, J

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This paper cites The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses

Reference 32

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics doi: 10.3233/faia241060

Reference 33

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This paper cites Reality Check Commentary: Krem- lin’s World-Class Dashboard Maximizes Disinfor- mation, at 26 Cents Per Lie, February 2024.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Reality Check Commentary: Krem- lin’s World-Class Dashboard Maximizes Disinfor- mation, at 26 Cents Per Lie, February 2024

Reference 34

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This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 35

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics OpenAI Red Teaming Network, September 2023

Reference 36

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics M., De Francisci Morales, G., and Bonchi, F

Reference 37

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Disrupting deceptive uses of ai by covert influence operations, May 2024

Reference 38

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Api pricing for openai models, 2024

Reference 41

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This paper cites On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Reference 42

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Quantifying the potential persuasive returns to political microtargeting

Reference 43

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Government Accountability Office

Reference 44

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This paper cites Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 46

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 47

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Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics workers at Russian troll farms earn 660 USD equivalent per month for writing 100 comments per day on social media

Reference 48

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This paper cites Who does(n’t) target you? mapping the worldwide usage of online political microtar- geting.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Who does(n’t) target you? mapping the worldwide usage of online political microtar- geting

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:45:43.470006Z

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Observation e6e56cd5-04fb-44c5-b621-7a97944a49c5 · outbound

This paper cites This work is licensed under a Creative Commons Attribution - Non Commercial - Share Alike 4.0 International License.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics This work is licensed under a Creative Commons Attribution - Non Commercial - Share Alike 4.0 International License

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:45:43.729937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7105e199-34c3-4f5c-82c6-b046a16c2c31 · outbound

This paper cites an unresolved cited work.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:45:43.643966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 564d86df-e706-4db3-bcb9-40c3a7c65cd7 · outbound

This paper cites an unresolved cited work.

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:45:43.757737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation ea849a78-7c5f-4c14-ba64-57a5ba25e935 · inbound

Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns cites this paper.

Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T11:35:39.906365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f7f3a6-d48a-4447-9ae0-b6f08d5398de · inbound

To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs cites this paper.

To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:05:50.875144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ceb28bd-1991-4a48-8583-d541c9e04c22 · inbound

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

Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:16:07.877820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99c10b7d-c1ef-48f9-a905-0596d0f40dea · 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 Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:47.518225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.