Paper Citation Record · LEDGER

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2308.12032.

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

pith.paper-citation-record.v1
2308.12032 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:58:03.196830Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T18:33:50.119173Z

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 06e5d5b4-6403-4720-b35d-f0979fd130fd · inbound

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning cites this paper.

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:03.196830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f02d9e-5609-4298-812c-1babb8b6ee4c · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.779354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bac7a134-0a52-4a39-b3fc-5a1e5f2ff06d · inbound

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud cites this paper.

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T21:13:58.117083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b731a121-028d-4a7b-ad2f-7c82c79c5cda · inbound

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud cites this paper.

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T21:13:58.122390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d678e190-52ed-4812-b55f-fa3ce86f956b · inbound

Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness cites this paper.

Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T19:54:51.985808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b61a3ac2-5ae1-43d3-8e0f-7cbe76ded128 · inbound

Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models cites this paper.

Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T13:44:15.446512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4b61e5c-e1fd-4943-b860-3eee95e3bf3a · inbound

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs cites this paper.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.870117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2bf98cd-5eae-40bb-ad2b-4f92178147ca · inbound

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis cites this paper.

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T11:59:28.303988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5596d7-b0e7-4e38-b72f-5ab3e205b463 · inbound

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline cites this paper.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T11:17:27.266132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67d16506-8c00-4edb-9dcd-180883516aa8 · inbound

A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation cites this paper.

A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T10:43:08.159936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75b67c2c-9d08-495d-b4ca-e5924485b7ea · inbound

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions cites this paper.

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 206

Resolution: unresolved; no resolver link, observed 2026-08-10T21:37:05.085746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e352668-96c1-4b6d-92a0-f96ef55bdbfe · inbound

R.I.P.: Better Models by Survival of the Fittest Prompts cites this paper.

R.I.P.: Better Models by Survival of the Fittest Prompts From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T23:02:23.333055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08e40cce-7785-46dc-a878-6922821ff89f · inbound

Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons cites this paper.

Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T10:26:06.907720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57921b79-6d7b-40e2-a015-2d587118a636 · inbound

A Comprehensive Survey on Imbalanced Data Learning cites this paper.

A Comprehensive Survey on Imbalanced Data Learning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 251

Resolution: unresolved; no resolver link, observed 2026-08-07T23:07:19.450634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 755a65f1-c9ee-420e-b1de-c12ed84ad7d4 · inbound

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model cites this paper.

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:02:23.882285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8185dbc5-4548-4b35-b7aa-1c461762f290 · inbound

Muon is Scalable for LLM Training cites this paper.

Muon is Scalable for LLM Training From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:02:52.179429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation afec0ce0-e291-4622-9b92-5ed6c9e9203b · inbound

Merge to Mix: Mixing Datasets via Model Merging cites this paper.

Merge to Mix: Mixing Datasets via Model Merging From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:35.884777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad3992c-dfb2-4810-af74-e6080a8d9bd5 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 239

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:13.357494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12b92e94-51eb-4fd0-80d3-53314c049101 · inbound

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis cites this paper.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:30:54.951220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e7f305-db1c-408e-8639-e83c44e0de5f · inbound

Efficient Data Selection at Scale via Influence Distillation cites this paper.

Efficient Data Selection at Scale via Influence Distillation From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:18.225316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f461a272-eee8-4f66-b90f-edd17cfd895f · inbound

Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models cites this paper.

Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:39:23.921553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ca8c4a2-3841-4b2c-9632-0b03d8b467b3 · inbound

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics cites this paper.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:04.732253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88b2e17a-7fa5-4ce3-9db6-c89937a0496f · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 133

Resolution: unresolved; no resolver link, observed 2026-08-06T14:13:06.422873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4339969f-0b5d-44a1-a95b-4cfccfd83260 · inbound

The Ratchet Effect in Silico: How Interaction Drives Cumulative Intelligence in Large Language Models cites this paper.

The Ratchet Effect in Silico: How Interaction Drives Cumulative Intelligence in Large Language Models From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-19T02:41:59.933306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 20b8d4f3-aa50-4441-930d-1de62a6c5da9 · inbound

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation cites this paper.

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T20:20:22.473706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b869c00-2632-44ba-8032-938821e53c52 · inbound

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms cites this paper.

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T20:17:47.522508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43e43177-ac86-4792-b8bc-86467cb66d3a · inbound

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains cites this paper.

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:02:40.415820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31e4a7f6-9428-49c7-96e0-9c0c97af855a · inbound

VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning cites this paper.

VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T19:46:20.079039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b88b3bd-52f3-4602-aa42-203c095036dd · inbound

Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning cites this paper.

Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:33:50.120744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77b9def2-5c09-4433-9cca-407f5d1d7264 · inbound

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement cites this paper.

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T13:54:03.879024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.