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Paper Citation Record · LEDGER

Scaling Laws for Neural Machine Translation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:42:39.979218Z

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-15T17:45:17.540282Z digest=sha256:da285b673091fb7e3876ace45d52e2819b86b2b992d4cd5fcc9c9d312efa7295

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-18T01:35:21.150772Z digest=sha256:a8600a2521e1595b3d3d1c138d4f655ec72faed32a4071ece43a27435ca200bf

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T07:59:55.849296Z digest=sha256:236f554b76eb947dba7eb6b66c008219aa965dc6dd148ebfa9a79195f91c341a

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-24T01:54:48.781227Z digest=sha256:6a87a2dc144866277efc467d63d66ff2115735aaadf8cfe43277e5267b0f22e2

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-16T18:44:49.519995Z digest=sha256:f70aafbae15f8b305e92591e2c2746718ba9d496051c7704e31206045db61af2

Observation 45ebde54-8f4b-4eba-9d8f-79fa3a0d0af0 · inbound

Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data cites this paper.

Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data Scaling Laws for Neural Machine Translation

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T20:42:39.979218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:42:39.979218Z digest=sha256:0214f88e469cb7afd3966d9fded75894274699b87ad564216fbdfd83803f9458

Observation e892bd07-98f0-4734-bac3-4ec26fb7872c · inbound

Neural Scaling Laws Rooted in the Data Distribution cites this paper.

Neural Scaling Laws Rooted in the Data Distribution Scaling Laws for Neural Machine Translation

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T18:30:15.501658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T18:30:15.501658Z digest=sha256:aeb926d2cab3ebf43149462f7a3616bf8dc197ab4d7cb20347dd3dbe64106e34

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-23T04:16:04.110552Z digest=sha256:27437cde0566e0ca78568078ec28647c457cb7046abfc66fa7287a4b31a59724

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.

source=pdf_text observed=2026-08-08T12:12:30.695454Z digest=sha256:689f0ee6075978bd501ecafe1407b6e90a4afa71b310e6208236e3c1954314be

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.

source=pdf_text observed=2026-08-07T13:01:08.302824Z digest=sha256:2ecac0f892daecc0e4dd03bbf6aa34687acefa1a1da3b7051ab9ffa783965d0a

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.

source=pdf_text observed=2026-08-07T05:23:30.657036Z digest=sha256:a0bb5629af89394752ab236c144c211e1e7aaee3557f27f883c8de89832ff456

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.

source=arxiv_source observed=2026-08-06T17:56:43.681631Z digest=sha256:ca662ee089c309d85bf18af406ff77c8cd9252590fe46986a895f60cfb652ea7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-29T14:14:25.876963Z digest=sha256:b74964d6fe2326f178bdb62b77e0bb598b8a74e54c79239e9085c4b972c92a68