Paper Citation Record · LEDGER

Instruction Pre-Training: Language Models are Supervised Multitask Learners

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.14491.

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

pith.paper-citation-record.v1
2406.14491 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:35:28.201418Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T17:42:47.476744Z

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 9891a7dd-a197-4500-aebb-99b61c6032b8 · inbound

Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis cites this paper.

Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T00:35:28.201418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0691dff5-2c15-4e52-8dd7-fb51a2729c7c · inbound

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

A Survey of LLM $\times$ DATA Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:12.947220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09129784-ddc4-4900-8e7b-6dd509a19915 · inbound

Localizing Persona Representations in LLMs cites this paper.

Localizing Persona Representations in LLMs Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T12:25:36.407520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 970e872e-43f5-4208-8d18-28c9c639f3b6 · inbound

Does Learning Mathematical Problem-Solving Generalize to Broader Reasoning? cites this paper.

Does Learning Mathematical Problem-Solving Generalize to Broader Reasoning? Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:34.395440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1750ef90-b37e-47b8-aae6-84a7d3c63dfc · inbound

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training cites this paper.

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T11:59:52.173406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e652c0d0-a0c1-44bc-9b72-edfcb66a5383 · inbound

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector cites this paper.

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:42:47.480310Z

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 ec71bfdf-bf7b-4515-a8d4-587a04b1b7f0 · inbound

How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification cites this paper.

How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-12T01:49:47.560255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.