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Paper Citation Record · LEDGER

MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2405.07467.

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

pith.paper-citation-record.v1
2405.07467 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 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 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:41:31.201021Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:49:54.644227Z

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 6cb3c946-6b53-4b1d-9952-d9308c6e00ae · inbound

CHESS: Contextual Harnessing for Efficient SQL Synthesis cites this paper.

CHESS: Contextual Harnessing for Efficient SQL Synthesis MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:24:22.996475Z

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.

source=arxiv_source observed=2026-05-19T11:24:22.789901Z digest=sha256:83ba0c1f4383afbc2bdc90b064c7b2ad4559b81956478d49fb48280458875436

Observation 46a0ed04-75d8-4365-9a9c-aa9bb955e4db · inbound

PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL cites this paper.

PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-08T21:41:31.201021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:41:31.201021Z digest=sha256:8e14e02d5f9264a6f5fb40dd515c27dcf06c79f72f0cc85e1ab2562590462c04

Observation 0365f857-634f-42a6-96ce-11d20adf29e9 · inbound

Automatic Metadata Extraction for Text-to-SQL cites this paper.

Automatic Metadata Extraction for Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T14:07:08.330616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:07:08.330616Z digest=sha256:96b9d5730711e49397f9e8ba4eb1b09004e1a394c90abf07bd7f5a52d02e1a2a

Observation c21280a1-69c2-4c12-9115-704c343f8766 · inbound

Knowledge Base Construction for Knowledge-Augmented Text-to-SQL cites this paper.

Knowledge Base Construction for Knowledge-Augmented Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T13:19:33.228541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:19:33.228541Z digest=sha256:eb3d6b414942ed55652c6987163859c843ca8afcd673cb0b7545448e2549af59

Observation f395457a-6151-4b1a-9482-ba44f7331b31 · inbound

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities cites this paper.

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T13:12:38.163368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:12:38.163368Z digest=sha256:072d55661d60827a8c9f125330510ebf737e74f47be0d3343d4097a02e511938

Observation ce953990-f8ce-4e32-9faf-f5cef0e7a9d8 · inbound

SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL cites this paper.

SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T12:10:09.934955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:10:09.934955Z digest=sha256:9dc6af4b51373ab142b5c89849265336a19397ec73c0a4fae9e405445ad56077

Observation fb7d5db1-5412-429b-ab4f-70b0db9fec17 · inbound

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages cites this paper.

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T11:58:35.805001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:58:35.805001Z digest=sha256:55e91990175653973f88bc0eaae925cd3bfb519dda7c1f518b2fc4007a5e0901

Observation 8b7e2a67-77bc-4134-b3b7-20179288372c · inbound

RAISE: Reasoning Agent for Interactive SQL Exploration cites this paper.

RAISE: Reasoning Agent for Interactive SQL Exploration MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:17.559129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:17.559129Z digest=sha256:7e8f8106a313783fb7a7b8da695fe1bb043dbad4acb675a0cf916349e627a872

Observation eb381734-1463-43e7-81a1-d5185d9499e6 · inbound

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL cites this paper.

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T10:46:27.040636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:27.040636Z digest=sha256:f474fde8bdb39611d6d64c120f52815efe57db5a97452c60ecde33806ac889a1

Observation 0c18eb8e-a45e-4295-8454-4fdc4edecebc · inbound

SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes cites this paper.

SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:46.984628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:43:46.984628Z digest=sha256:71f75b57a92b11dc67331c5c9812baa73122a32fc117ecd790bf2957696f3f82

Observation a35100ae-d37d-48e9-866d-c6bbae8882fc · 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 MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T05:38:16.331686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:38:16.331686Z digest=sha256:0fe79a8425ff0be749525c8ade3d8ffb51bc134d5fbf0a9f72e33f9e4762262b

Observation 60a83102-439d-46af-b9c7-67334ecfa895 · inbound

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL cites this paper.

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T06:52:59.879026Z

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.

source=pdf_text observed=2026-05-19T06:52:57.718359Z digest=sha256:9e57d65c6cc04d081bb4e130f9abe1bfeed8e37019a732b0fbfb7682dc01cc0e

Observation 0564bbaa-4796-44e5-b696-4bba1ff840d6 · inbound

Text-to-SQL for Enterprise Data Analytics cites this paper.

Text-to-SQL for Enterprise Data Analytics MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:53.807366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:09:53.807366Z digest=sha256:36711e635134fdd3d627dc75fa601c6c5b0d8074d88eb21212d3ee5f384422b1

Observation dc253fd7-eed9-4ffd-8cb2-4498b39ad9b9 · inbound

SLM-SQL: An Exploration of Small Language Models for Text-to-SQL cites this paper.

SLM-SQL: An Exploration of Small Language Models for Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T11:44:35.221158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:44:35.221158Z digest=sha256:720cfab1204dcb966b7533fca48d9f351d78f4acef3ca5a6709346dd94005e41

Observation a79d8036-9d23-4052-8bc3-b0c2e95176d5 · inbound

RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL cites this paper.

RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T11:09:17.757730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:09:17.757730Z digest=sha256:f7659fafd66a6e096838691292b063533387b2311e0b8e2093ee0283973453ea

Observation 9ab48522-253a-47a5-ba94-044d64ba3569 · inbound

Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding cites this paper.

Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:11:27.094904Z

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.

source=pdf_text observed=2026-05-07T07:35:50.936221Z digest=sha256:d6ba6b05b48271ff6052a984df2c0d32fba18dd31a8b37a10878d693d1d4022f

Observation 0f497e56-20f0-4703-ab67-f14719a78fa6 · inbound

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation cites this paper.

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T03:25:58.176358Z

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.

source=arxiv_source observed=2026-05-11T02:28:20.366674Z digest=sha256:83ae39e3c34de319dc06d9278fd54534766efc770725d671e077fa540ae80e76

Observation 90831014-a0dc-48ae-9e80-adbce59fab08 · inbound

EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries cites this paper.

EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-04T14:49:54.645733Z

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.

source=pdf_text observed=2026-06-26T02:38:20.300232Z digest=sha256:1903c2290823af2e1501df320225c51d62efe4dc6dd34fae66aeca42318b71b0

Observation b11c56f0-b0de-4921-a862-9d3446805190 · inbound

EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries cites this paper.

EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-04T02:32:09.630685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T02:32:09.630685Z digest=sha256:278c6dd12e27ae9514e2334fb46ad4f66678df48a8cb5ec3921ca3a2db5bb6c2