Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.07074.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:04:56.831307Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T08:02:23.953711Z
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 cdf62744-0341-4cda-b3a3-a73b6b45297c · 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 #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Reference 107
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T08:02:23.956798Z
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 d348111f-63c5-4317-b534-5c700ed508b9 · inbound
Seed-Coder: Let the Code Model Curate Data for Itself cites this paper.
Seed-Coder: Let the Code Model Curate Data for Itself #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:56.831307Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 115596c1-3ea2-4d5d-96d6-837772734955 · inbound
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs cites this paper.
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:37:08.020597Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc73fa35-3b83-49fe-b103-86f4fd33298a · inbound
ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries cites this paper.
ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:29:08.196359Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2959688f-2eab-42c0-91fe-a0d86eaec9e9 · 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 #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:54:04.051591Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.