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Paper Citation Record · LEDGER

NumGPT: Improving Numeracy Ability of Generative Pre-trained Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.03137.

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

pith.paper-citation-record.v1
2109.03137 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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:57:56.011083Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T00:25:19.849309Z

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 86df1ab2-eb45-41da-ad12-0ae653704026 · inbound

Causality for Natural Language Processing cites this paper.

Causality for Natural Language Processing NumGPT: Improving Numeracy Ability of Generative Pre-trained Models

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-16T11:57:56.011083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:57:56.011083Z digest=sha256:6f787bf6bb309d84a8b06781c143cb6b911ead00f3a378e573784a526a27a3b0

Observation 2ea6a845-7749-4ff6-ba56-3d7d4c5c3b66 · inbound

Generative Optimization for Incentivized Advertising with Global Level Constraints cites this paper.

Generative Optimization for Incentivized Advertising with Global Level Constraints NumGPT: Improving Numeracy Ability of Generative Pre-trained Models

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:25:19.854077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T00:25:19.377922Z digest=sha256:8616e3ded9d18538ee4707c5fccae1c4ad6ab954ae2dd12025f88aee734f4725