Paper Citation Record · LEDGER

Noisy Channel Language Model Prompting for Few-Shot Text Classification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2108.04106.

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

pith.paper-citation-record.v1
2108.04106 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:51:49.996466Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:28:38.267362Z

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 40499377-af10-4a90-b0d0-461bb021970b · inbound

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cites this paper.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 225

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:51:46.890180Z

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 05d66ed5-524e-4f5e-bb33-2241547deeb4 · inbound

Emergent Abilities of Large Language Models cites this paper.

Emergent Abilities of Large Language Models Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:38:38.081729Z

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 c6871c72-d3f7-45fb-b654-b21749b4067a · inbound

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs cites this paper.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.996466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b3032fc-7a54-4e7e-a46a-6957dcab947b · inbound

Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection cites this paper.

Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:30:37.451165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26dd193-2540-4f10-91a6-0c54d7f4e4b0 · inbound

Neuron-Aware Active Few-Shot Learning for LLMs cites this paper.

Neuron-Aware Active Few-Shot Learning for LLMs Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T16:28:38.272294Z

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.