Paper Citation Record · LEDGER

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

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:12:12.768405Z

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 2026-08-06T06:34:29.942622+00:00.

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 2026-08-06T06:34:29.942622+00:00.

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 2026-08-06T06:34:29.942622+00:00.

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 2026-08-06T06:34:29.942622+00:00.

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 2026-08-06T06:34:29.942622+00:00.

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.

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 2026-08-06T06:34:29.942622+00:00.

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 2026-08-06T06:34:29.942622+00:00.