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Paper Citation Record · LEDGER

Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.19249.

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

pith.paper-citation-record.v1
2305.19249 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 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:03:49.794450Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T07:18:07.001231Z

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 41dcd886-5c8e-49df-837f-5a78bf86a5be · inbound

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations cites this paper.

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-08T17:03:49.794450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:03:49.794450Z digest=sha256:8ba8c978491a17401a66c1090a4807250de8b7c865c0cc750eb4bffcdbb1affc

Observation 6b62ced0-8219-423e-b287-b378f7d0dacf · inbound

Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis cites this paper.

Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-08T16:33:53.577675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:33:53.577675Z digest=sha256:fcf4a8d317b58866327a0b6176eb79d8a6e98a4d150192fbfb4bf16433a96cde

Observation 6a428f17-441c-4495-b920-10589e7e418f · inbound

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability cites this paper.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T12:40:21.446432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:21.446432Z digest=sha256:c92d27f0788e48bb0d79d36e47faf074f2bf2507a9bb998ba3a6e2738590aa63

Observation b851a32c-b487-4a7b-b4ff-a4f29c10ff30 · inbound

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered cites this paper.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T05:38:25.847025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:38:25.847025Z digest=sha256:8e99255afbed1a42815b73a6b573236f291b3d160deff2aa0b1b8a7ffeeb33be

Observation 508e78f9-fa73-48d0-9ba2-bb3920c27b89 · inbound

Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-06T22:44:44.002754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:44:44.002754Z digest=sha256:c818aedbe1a014a24cfe3a9d8eb951abc8d043cade84aa53b99c0c9374c12868

Observation 490c1a7e-70cc-4aae-bfb3-da5932207987 · inbound

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models cites this paper.

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-07T10:18:52.749933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:18:52.749933Z digest=sha256:9c29d2a9a09ca569459a736e67a34a8484ebe04b0705d730b9d77e29d43ceb4f

Observation 0a2ad652-dbb3-4615-91d5-78c3c7c368f6 · inbound

Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates cites this paper.

Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:18:07.003009Z

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=2026-05-20T07:14:59.396900Z digest=sha256:aa040cec0992c5b33563934d5493051b518b54dfce338981c164aa0a182f1df8

Observation 9363f261-8615-4810-b1a8-b28d4011c611 · inbound

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings cites this paper.

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-07-14T07:42:06.462698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T07:42:06.462698Z digest=sha256:9a55a112fd0b16c8fcd9d7e65e05d6d87098d7a5fc8366f395e386a1b9ba5557

Observation 6174ec00-a6bd-4ed3-a318-c8d8c68cea11 · inbound

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings cites this paper.

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T07:07:57.295329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T07:07:57.295329Z digest=sha256:ab701dfb1ff9229d3aa3a474002bcb015818e7d6ebae078b96c22de13de33f0e