Paper Citation Record · LEDGER

Calibrating Large Language Models Using Their Generations Only

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.05973.

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

pith.paper-citation-record.v1
2403.05973 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:28:40.226187Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 00d15bc0-fc6f-479a-ac5c-81f62b2f2838 · inbound

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions cites this paper.

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions Calibrating Large Language Models Using Their Generations Only

Reference 208

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:55.143106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6c84453-3adc-4b8f-b4a8-74a5456591b0 · inbound

Logits are All We Need to Adapt Closed Models cites this paper.

Logits are All We Need to Adapt Closed Models Calibrating Large Language Models Using Their Generations Only

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T14:18:05.711182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57df0d9b-1789-48d4-802d-825d66b02fc2 · inbound

Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning cites this paper.

Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning Calibrating Large Language Models Using Their Generations Only

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T12:28:40.226187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e309f54-941b-4124-b3bf-7b49547dea3c · inbound

Towards Harmonized Uncertainty Estimation for Large Language Models cites this paper.

Towards Harmonized Uncertainty Estimation for Large Language Models Calibrating Large Language Models Using Their Generations Only

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:22.503978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e177614a-fa33-4e22-bd4b-d0d1c9efca72 · inbound

Toward Better Generalisation in Uncertainty Estimators: Leveraging Data-Agnostic Features cites this paper.

Toward Better Generalisation in Uncertainty Estimators: Leveraging Data-Agnostic Features Calibrating Large Language Models Using Their Generations Only

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:04.556267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cdec345-3fa4-4bd6-ba62-80192386feb1 · inbound

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models cites this paper.

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models Calibrating Large Language Models Using Their Generations Only

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T18:53:02.754686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137c863d-ddbf-49e0-bbd0-b1d09ffa628f · inbound

Calibrating Model-Based Evaluation Metrics for Summarization cites this paper.

Calibrating Model-Based Evaluation Metrics for Summarization Calibrating Large Language Models Using Their Generations Only

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:41:37.058549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03a3e670-c9ff-4226-bab7-a0c7ec968104 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Calibrating Large Language Models Using Their Generations Only

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-09T20:37:32.099962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b61d9940-678d-4e36-9932-9331beb47676 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Calibrating Large Language Models Using Their Generations Only

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.032998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.