Paper Citation Record · LEDGER

A Survey on Employing Large Language Models for Text-to-SQL Tasks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2407.15186.

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

pith.paper-citation-record.v1
2407.15186 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:26:57.581628Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 cb631ec2-e0ca-41c6-8f93-642c7f1c21cc · inbound

Meta-aware Learning in text-to-SQL Large Language Model cites this paper.

Meta-aware Learning in text-to-SQL Large Language Model A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:57.581628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd2ee94-8e8d-40df-ae95-99809a23204d · inbound

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation cites this paper.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.278842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423e2eae-dbff-462e-856b-f3144e41386e · inbound

SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists cites this paper.

SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.016771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11eda916-c3fd-4949-99bd-430b8a13c936 · inbound

Bootstrapping Learned Cost Models with Synthetic SQL Queries cites this paper.

Bootstrapping Learned Cost Models with Synthetic SQL Queries A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T15:31:15.286631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb44deb7-f1ee-427e-a3c2-ab04e345c6a7 · inbound

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs cites this paper.

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 192

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:45:59.628421Z

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.

Observation c2916849-c90a-42e5-bbb4-43851a1ac54e · inbound

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning cites this paper.

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 177

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:01:00.315037Z

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.

Observation 02f7889e-50f4-4fe4-9b08-7230a6dcb506 · inbound

LogCopilot: Automating Log Aggregation Analysis through Large Language Models cites this paper.

LogCopilot: Automating Log Aggregation Analysis through Large Language Models A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-27T04:20:31.790017Z

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.