Paper Citation Record · LEDGER

Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2305.09246.

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

pith.paper-citation-record.v1
2305.09246 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:54:32.353949Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a17b9d42-079a-4054-9af9-99bd1de6bf93 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 184

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:28:39.584384Z

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 3b3810f5-0a64-4834-a2fa-fc7cec50e540 · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.935697Z

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 8799ccc2-9085-41d3-9fbe-10ed33cfcfc9 · inbound

Can Large Language Models Be Query Optimizer for Relational Databases? cites this paper.

Can Large Language Models Be Query Optimizer for Relational Databases? Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T18:54:32.353949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4df64c3e-463d-4036-8ef3-d208509a3a8f · inbound

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation cites this paper.

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:48:02.545145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fcd1e42-e9fc-4010-b60f-aae0e4afd8d4 · inbound

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models cites this paper.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:28.279489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5508cc0-8a67-49c5-9e72-5f3b9356a530 · inbound

Towards Efficient and Effective Alignment of Large Language Models cites this paper.

Towards Efficient and Effective Alignment of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:35.255414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a36c054a-8148-4258-b397-d8431c44bc66 · inbound

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation cites this paper.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.256358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f8ba62-0d74-4f2a-93fa-901086584fb9 · inbound

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection cites this paper.

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:39.307095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d40b9ff7-d536-4acf-9353-ca8e723307c4 · inbound

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving cites this paper.

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 110

Resolution: unresolved; no resolver link, observed 2026-08-04T20:55:41.527188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e20ff72-985d-4a07-8e15-ce9c79849849 · inbound

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) cites this paper.

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T23:10:59.419614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc571b7-546d-4855-b965-92104cab70fc · inbound

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization cites this paper.

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:30:48.972081Z

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 0d257c10-9733-40ce-8402-a1a36c6abb3e · inbound

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance cites this paper.

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:56:47.928366Z

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 b8741f3e-86b9-44f7-a692-e87c6dbefe66 · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:08.034102Z

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 f5bd5196-42f2-4383-a605-f82d92ada5f5 · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:41:17.650267Z

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.