Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 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 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:26:06.907720Z

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 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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.