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Paper Citation Record · LEDGER

D4: Improving LLM Pretraining via Document De-Duplication and Diversification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2308.12284.

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

pith.paper-citation-record.v1
2308.12284 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:33:55.009360Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:38:15.869750Z

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 48bd6354-d573-4255-92f0-aaf829d0d97c · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 252

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T22:46:40.502583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T22:46:39.268353Z digest=sha256:ffd373146baf6d1e4f6f3b74755188dd27b3e701f5efda53d694358b4401cb17

Observation 7ba6fee9-d950-4ee2-9fc1-cab5b0dac66c · inbound

Demystifying CLIP Data cites this paper.

Demystifying CLIP Data D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:20:20.244672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T09:20:20.143143Z digest=sha256:90d25b4406e95cb2a6de39a0ff2391a22f23672a07d1e284273f9ddf16cadd26

Observation 297283e5-f43c-4334-b970-b5879146e14f · inbound

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP cites this paper.

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:38:15.872289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-23T17:36:18.451771Z digest=sha256:c4d3302eb1a861abe4d5e18cd0b99174d7f4cdc94eb6786a499da91d09a2cd48

Observation cc034977-d9fb-489d-9ff7-ad39c71ea6ce · inbound

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain cites this paper.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T15:33:55.009360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:33:55.009360Z digest=sha256:d88707c782896344d14127eda20c44f47aa8020f27ddb32bf59f4e842344385d

Observation 6372e10b-5c5e-4b4b-a850-ae7c47d3a634 · inbound

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector cites this paper.

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-18T17:42:47.546454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T17:39:17.456350Z digest=sha256:64fdb3997148b03c50557409116f674a032b89815170c3fc3cf9b88cf1cdaeb0

Observation ee7dfd12-5d64-48d5-b10c-59e6af1e6360 · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-10T08:48:01.008180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T08:47:36.122054Z digest=sha256:9c23768d4c6168a753705bbb3e272f83d299a9f3e24a06bdff32bfce7496fde7

Observation ae629a64-7b00-43de-a6bf-d23156b206a2 · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-02T16:12:12.768405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:12:12.768405Z digest=sha256:38d8e0a8a3f8aacaa35f83ee530f4c9ec202b39862bc4b40c2cb0d8177f76967

Observation 69337ef1-a647-4b0c-8979-f40c380d0ae3 · inbound

Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks cites this paper.

Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:24.166794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T04:18:11.836537Z digest=sha256:aa062cbe73a6b461e0382f7afcf5b68fcadd525ec135010ebef21127a96684be

Observation 3865ca08-8af6-4f3c-afd7-a546f6bfdd0c · inbound

Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference cites this paper.

Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:31:29.028876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T04:11:02.387702Z digest=sha256:e929430d2e4b18cdb7b27ef9b9f1b5bb3de7637f7b0d5ae1873649c869a0017d