Paper Citation Record · LEDGER

Towards Zero-Label Language Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2109.09193.

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

pith.paper-citation-record.v1
2109.09193 v1

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 2026-08-08T19:08:57.632991Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T12:14:26.606604Z

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 6e6e5580-e87f-4b3e-8370-6e005cb4791f · inbound

Ethical and social risks of harm from Language Models cites this paper.

Ethical and social risks of harm from Language Models Towards Zero-Label Language Learning

Reference 288

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:24:30.071200Z

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 30bd5f71-2927-41e8-8907-6617bc661170 · inbound

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model cites this paper.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Towards Zero-Label Language Learning

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-24T12:14:26.610742Z

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 539c65e7-649b-4f6a-b1b2-4c16644e4f47 · inbound

RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback cites this paper.

RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback Towards Zero-Label Language Learning

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:32:27.943611Z

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 4fac625b-b32c-47b6-af5c-12448d574bc8 · inbound

Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning Towards Zero-Label Language Learning

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-17T12:04:10.632553Z

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 3a54baaf-f297-4d69-a577-1865744b8a86 · inbound

DeepThink: Aligning Language Models with Domain-Specific User Intents cites this paper.

DeepThink: Aligning Language Models with Domain-Specific User Intents Towards Zero-Label Language Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T19:08:57.632991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a976e859-7b8f-4138-908d-3b0c13b6c839 · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Towards Zero-Label Language Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:52.304605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165ec744-088b-4e7f-9886-c276551037e3 · inbound

TabEmb: Joint Semantic-Structure Embedding for Table Annotation cites this paper.

TabEmb: Joint Semantic-Structure Embedding for Table Annotation Towards Zero-Label Language Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:41:02.216894Z

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.