Paper Citation Record · LEDGER

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2510.00866.

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

pith.paper-citation-record.v1
2510.00866 v4

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:24:16.848373Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T06:12:37.854507Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:15:23.717436Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 132456d3-85e9-4405-ab8d-f5edfa56e199 · outbound

This paper cites A Survey on Data Selection for Language Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining A Survey on Data Selection for Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.114391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aa34b9d-a3a7-471e-98f3-d4ccc83a7a2e · outbound

This paper cites an unresolved cited work.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.765132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e873b667-efa3-4f07-8a8d-a2c6da1c2163 · outbound

This paper cites FastText.zip: Compressing text classification models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining FastText.zip: Compressing text classification models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.097332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d03ef3e1-bbea-455b-bd8c-c8227eb799cc · outbound

This paper cites Smith, and Hannaneh Hajishirzi.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Smith, and Hannaneh Hajishirzi

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.294758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b6baefb-99bc-41d4-a233-445ea04bbc62 · outbound

This paper cites We show in Figure 12 the result of such experiments, averaged across 3 runs.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining We show in Figure 12 the result of such experiments, averaged across 3 runs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.644460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c7981b-2cbe-443f-b2ef-11f119f87ab2 · outbound

This paper cites A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.423969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c67621af-dfe5-45fb-938b-2407d5a6dbaf · outbound

This paper cites Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.511639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c469e8c4-8146-4eac-b3bb-a10c2979fd45 · outbound

This paper cites Language Models Improve When Pretraining Data Matches Target Tasks.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Language Models Improve When Pretraining Data Matches Target Tasks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.661029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 112cd137-9539-461b-a0ea-ebfd127493e6 · outbound

This paper cites Scaling laws for optimal data mixtures.arXiv preprint arXiv:2507.09404,.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Scaling laws for optimal data mixtures.arXiv preprint arXiv:2507.09404,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.833846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 709deb01-6701-4b24-bdb9-b2d617919c4c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining LLaMA: Open and Efficient Foundation Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.918187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921ee0b3-5867-4f66-8493-f4513abe37fd · outbound

This paper cites Dataset Distillation.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Dataset Distillation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.003251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7be026f3-352a-46e8-9251-8928f11befc5 · outbound

This paper cites This aligns with recent concurrent work from Mizrahi et al.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining This aligns with recent concurrent work from Mizrahi et al

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.848373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0906d0a-d16e-4ec4-8e4c-d10d13caeb6d · outbound

This paper cites Challenges in detoxifying language models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Challenges in detoxifying language models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.275381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9496160c-eb4d-48cd-9cfc-274fa04b2015 · outbound

This paper cites Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.399318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1afdabb6-49b3-4dd5-9f2b-d02976ddb773 · outbound

This paper cites an unresolved cited work.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.520246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abef4e02-24e7-41b9-9b18-f3a8134dab3e · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.274285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26dbe4ed-f434-47e7-9f17-8ffccf83c58d · outbound

This paper cites Mission: Impossible Language Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Mission: Impossible Language Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.278209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db8573d-6eb0-40f1-b40f-771b4457ad11 · outbound

This paper cites Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:16.160727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2100c74a-0bdc-469c-bb34-7a4b140d3968 · outbound

This paper cites Unearthing Large Scale Domain-Specific Knowledge from Public Corpora.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Unearthing Large Scale Domain-Specific Knowledge from Public Corpora

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.696044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c14b2b2-1f5f-4504-ade3-1711f2c7577f · outbound

This paper cites Training Compute-Optimal Large Language Models.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Training Compute-Optimal Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.969870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c0ce1d4-043a-4b9a-98fa-32ed34c99681 · outbound

This paper cites ELI5: long form question answering.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining ELI5: long form question answering

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.595837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 290c9267-7cf4-468f-bf7e-8f1d673c9722 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.412123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1f66cfd-4456-4ec6-a630-ddb72b9ecda1 · outbound

This paper cites Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:14.800183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abfd3374-d66b-4140-9050-a5b30258c00f · outbound

This paper cites Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Scott Keh, Kushal Arora, et al.

Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Scott Keh, Kushal Arora, et al

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T13:24:15.305088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 19ae4bf0-f888-4d1c-a90d-f551d0120a32 · inbound

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering cites this paper.

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-24T01:14:22.399339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.