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Paper Citation Record · LEDGER

Distractor generation for multiple-choice questions with predictive prompting and large language models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.16338.

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

pith.paper-citation-record.v1
2307.16338 v1

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:20:17.679991Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T05:56:11.268275Z

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 cb00ef13-f91c-4a82-ac67-dd4dde68a7a1 · inbound

Benchmarking the Pedagogical Knowledge of Large Language Models cites this paper.

Benchmarking the Pedagogical Knowledge of Large Language Models Distractor generation for multiple-choice questions with predictive prompting and large language models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:17.679991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:17.679991Z digest=sha256:1d1d3c1447dc7e15e95a9ddc838237019e4dbea2f91c1985e1fc0d947687c2bd

Observation deacdf92-e768-4360-8829-d678a424c3c4 · inbound

Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation cites this paper.

Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation Distractor generation for multiple-choice questions with predictive prompting and large language models

Reference 116

Resolution
verified exact
arxiv_id, observed 2026-05-10T05:56:11.269528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-10T05:52:38.442341Z digest=sha256:108b33d106a28620335b18df07d16b28ccae61b96f1799af847ed7b2e29d95cd