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Paper Citation Record · LEDGER

AraGPT2: Pre-Trained Transformer for Arabic Language Generation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.15520.

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

pith.paper-citation-record.v1
2012.15520 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:11:11.188883Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T00:24:47.227482Z

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 eec9bd77-5944-4aa2-95bd-647647df829c · inbound

Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks cites this paper.

Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks AraGPT2: Pre-Trained Transformer for Arabic Language Generation

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T14:11:11.188883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:11:11.188883Z digest=sha256:47e24ef2aa3ee05d44af642f560266a9f0d6168b42ed22ebb5fd2c47e60a31cf

Observation d425fdf5-357e-4d81-b8d3-f36f4bd9c306 · inbound

Machine learning and emoji prediction: How much accuracy can MARBERT achieve? cites this paper.

Machine learning and emoji prediction: How much accuracy can MARBERT achieve? AraGPT2: Pre-Trained Transformer for Arabic Language Generation

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:24:47.228728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T00:08:14.010504Z digest=sha256:ef056c0a5ce50938175359fd45e14b6d83c8bec019b2183e0f37b85e4cd7c163