Pith. sign in

Paper Citation Record · LEDGER

How Useful are Educational Questions Generated by Large Language Models?

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.06638.

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

pith.paper-citation-record.v1
2304.06638 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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T18:03:07.646917Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c4554bbd-a46d-4efc-bb0d-61764ca94549 · inbound

ActuBench: A Multi-Agent LLM Pipeline for Generation and Evaluation of Actuarial Reasoning Tasks cites this paper.

ActuBench: A Multi-Agent LLM Pipeline for Generation and Evaluation of Actuarial Reasoning Tasks How Useful are Educational Questions Generated by Large Language Models?

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:46:05.181035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-10T00:35:24.397273Z digest=sha256:a7d2e0f0a8df08eedab0e2d4d44f815b7d977866c41220d1669b12b292603c6f

Observation 0fa4418e-9117-4623-adce-f4811d1eb8ca · inbound

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures cites this paper.

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures How Useful are Educational Questions Generated by Large Language Models?

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-20T18:03:36.695648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-20T18:03:07.646917Z digest=sha256:744434800b99959e05e2a07dd727b4720f4e67ef5f1f779210b0410f19b80d9c