Paper Citation Record · LEDGER

Generator-Retriever-Generator Approach for Open-Domain Question Answering

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.11278.

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

pith.paper-citation-record.v1
2307.11278 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 2026-08-07T14:05:17.173975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T13:32:17.431518Z

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 a47596d4-638c-410f-a8c9-98c30b2afb5d · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 164

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:32:17.433424Z

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 28e4d4fd-efb9-42c1-9c84-da1b3b43bda4 · inbound

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers cites this paper.

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:17.173975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56d484e6-4723-4307-9232-08197849306d · inbound

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques cites this paper.

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T19:37:18.009132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cab0fac7-067c-43b1-85e5-79c94ea6d034 · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T16:24:22.887351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23bd47cb-e809-4cfb-8e98-82f7510790ba · inbound

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.218967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.