Paper Citation Record · LEDGER

Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.13160.

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

pith.paper-citation-record.v1
2305.13160 v2

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 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:45:43.191396Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T04:34:35.305867Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 d5592b88-58c9-4709-b4a9-e589b0fe4615 · inbound

Simple synthetic data reduces sycophancy in large language models cites this paper.

Simple synthetic data reduces sycophancy in large language models Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-16T14:48:08.713557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7dc50bd8-cb66-4f96-b657-1422c089c494 · inbound

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning cites this paper.

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:46:39.486599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation faaaa970-02ec-44d4-be60-9ddfed7302c0 · inbound

Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics cites this paper.

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

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T04:45:43.191396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60e87c6e-acdc-4666-9514-a8be478ff5d8 · inbound

CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks cites this paper.

CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:40:57.359166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 235258ff-99f6-432f-81a2-93df029e39c2 · inbound

Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions? cites this paper.

Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions? Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T10:16:51.264870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5b41146-aca3-4a49-9d22-b267c1e72740 · inbound

Reinforced Preference Optimization for Reasoning-Augmented Recommendations cites this paper.

Reinforced Preference Optimization for Reasoning-Augmented Recommendations Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T04:34:35.313310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.