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Paper Citation Record · LEDGER

Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.08635.

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

pith.paper-citation-record.v1
2212.08635 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:40.447199Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

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 2e3e34b3-2ead-4733-95aa-1fd357d1d141 · inbound

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds cites this paper.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T11:17:40.447199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:17:40.447199Z digest=sha256:3bcb49d6dba63037191f4fe36bc9b1bf65ea024d8b97e6d30d202c5a2c52051d

Observation 23d3d0b9-f0db-4648-8fef-8789b05f1e9b · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:56:39.032496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T22:56:33.122478Z digest=sha256:6873ea637b8f0293fe22022945e7ae8d193a1e5bd54ca95208ccfe0812ead884