Paper Citation Record · LEDGER

Large Language Models Are Zero-Shot Text Classifiers

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2312.01044.

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

pith.paper-citation-record.v1
2312.01044 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:35:28.487477Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:28:00.077676Z

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 e04ac125-170c-4bbf-9cad-8eb20172bf35 · 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 Large Language Models Are Zero-Shot Text Classifiers

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T00:35:28.487477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d707f62f-f107-46a0-83a7-207a82dad2d6 · inbound

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs cites this paper.

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs Large Language Models Are Zero-Shot Text Classifiers

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:20:56.121461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b8ae55a-edca-4513-9718-21e241d6fe7d · inbound

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines cites this paper.

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines Large Language Models Are Zero-Shot Text Classifiers

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:21:01.640725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfe4cf65-a182-48c9-a455-c8cc6aa3f12e · inbound

ToxiShield: Promoting Inclusive Developer Communication through Real-Time Toxicity Filtering cites this paper.

ToxiShield: Promoting Inclusive Developer Communication through Real-Time Toxicity Filtering Large Language Models Are Zero-Shot Text Classifiers

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:46:17.325585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a66635c8-d528-43f2-9ca2-3bf7485c702f · inbound

BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration cites this paper.

BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration Large Language Models Are Zero-Shot Text Classifiers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:28:00.079206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 280f9e99-548a-4ec2-bc87-e03dde4b8ee6 · inbound

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes cites this paper.

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes Large Language Models Are Zero-Shot Text Classifiers

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T07:49:38.585839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.