Paper Citation Record · LEDGER

Scaling Laws for Neural Machine Translation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2109.07740.

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

pith.paper-citation-record.v1
2109.07740 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:12:30.695454Z

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

19
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 7c279b5b-bb6c-4555-80a1-db7b76935550 · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Scaling Laws for Neural Machine Translation

Reference 124

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:45:17.791463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e98c2853-cd4b-45dc-ab15-872aa187c3b3 · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Scaling Laws for Neural Machine Translation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:35:21.619989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c03dddc1-7ccd-4665-8a17-5cecf311a4ec · inbound

Reinforced Self-Training (ReST) for Language Modeling cites this paper.

Reinforced Self-Training (ReST) for Language Modeling Scaling Laws for Neural Machine Translation

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:59:56.008047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fe0ca98-a977-49cc-8ecf-9e831ad29a84 · inbound

Scaling and renormalization in high-dimensional regression cites this paper.

Scaling and renormalization in high-dimensional regression Scaling Laws for Neural Machine Translation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:55:55.094304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7450ff08-f62a-402d-92dd-ab5e24db16d1 · inbound

Lessons from the Trenches on Reproducible Evaluation of Language Models cites this paper.

Lessons from the Trenches on Reproducible Evaluation of Language Models Scaling Laws for Neural Machine Translation

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:44:49.725793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b523248-fc7c-495e-9405-61a0e3e4437b · inbound

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models cites this paper.

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models Scaling Laws for Neural Machine Translation

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:17:30.967158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d30854b-3bce-42e1-adf2-ffa530c19a88 · inbound

Scaling Pre-training to One Hundred Billion Data for Vision Language Models cites this paper.

Scaling Pre-training to One Hundred Billion Data for Vision Language Models Scaling Laws for Neural Machine Translation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T12:12:30.695454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1382cf42-2a8d-4721-a172-7a1bbde7e19e · inbound

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks cites this paper.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Scaling Laws for Neural Machine Translation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:08.302824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acc9f254-aae1-4d37-967b-443e036820b3 · inbound

Scaling Laws of Motion Forecasting and Planning -- Technical Report cites this paper.

Scaling Laws of Motion Forecasting and Planning -- Technical Report Scaling Laws for Neural Machine Translation

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:30.657036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a285115f-3246-4ba1-b8a4-261f5b57b4fd · inbound

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs cites this paper.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws for Neural Machine Translation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.681631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbe6605a-7ba2-4d33-a680-6302b2832113 · inbound

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization cites this paper.

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization Scaling Laws for Neural Machine Translation

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:23:30.992298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.