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Paper Citation Record · LEDGER

tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2301.05948.

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

pith.paper-citation-record.v1
2301.05948 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:36:07.068255Z

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

3
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 168e1298-cd41-4597-849a-aa49f25b016e · inbound

Recipient Profiling: Predicting Characteristics from Messages cites this paper.

Recipient Profiling: Predicting Characteristics from Messages tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T13:36:07.068255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:36:07.068255Z digest=sha256:f963f934d314764bc02af3a9a89e9c0ecb95a0e89be6aa9ea7cea1fa5654087a

Observation eecb8ac0-e54e-4f56-b05b-9a4354ca9c14 · inbound

R-TOFU: Unlearning in Large Reasoning Models cites this paper.

R-TOFU: Unlearning in Large Reasoning Models tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:09.883297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:09.883297Z digest=sha256:6487606e2df7ca5a84a68daa8903b5c894e0b385bb3179c3c86e23da02bdcbf2

Observation 9a684d4c-e6a6-410d-9b40-27e0eed2c725 · inbound

FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain cites this paper.

FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T11:49:36.922834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:49:36.922834Z digest=sha256:708231db34b6158448f373127a88fae6faa832a66c7ae3ab516847d88972ef35

Observation 2b1dcf90-aaa8-4d58-84ae-609676065e07 · inbound

Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding cites this paper.

Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-03T23:39:09.021908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:39:09.021908Z digest=sha256:7c57c45f5339d4bd75283ff7f62e88d1144343506828a06f2abb940afd15020f

Observation 4024e3bb-8105-4f44-8a36-1e5711ac9c57 · inbound

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning cites this paper.

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation

Reference 196

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:23:12.805955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-19T20:22:55.750693Z digest=sha256:64ae928a2621e6110260a402f2e923cb4eeb96670f9446a13633cb71586a576f