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Paper Citation Record · LEDGER

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges

As of 12 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2412.05208.

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

pith.paper-citation-record.v1
2412.05208 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:52:22.017799Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T06:49:23.432383Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:36:45.325817Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7235be67-be34-4b57-bfc5-600397e0d5e8 · outbound

This paper cites Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.894229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.847789Z digest=sha256:cf4bb14bf4bb8972e6d7257398c497d9093af6b01ccf1413fba1352248d87cbb

Observation f77e6359-b64f-4208-8a34-f4620637ac48 · outbound

This paper cites Exploring language models: A comprehensive survey and analysis,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Exploring language models: A comprehensive survey and analysis,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.870841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.865466Z digest=sha256:8477350f694b257ef2c5c5a3db26c0108e7a3d1c870c6c5192745c6cbdbff14a

Observation aa4548b2-e300-495b-a627-8d93398cdb7f · outbound

This paper cites Next-generation database interfaces: A survey of llm-based text-to-sql,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Next-generation database interfaces: A survey of llm-based text-to-sql,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.875591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.875591Z digest=sha256:d00c3df4c9743964ee81cfd7c45b3f8ff99948bcbf1cc7f4cb7d71dc73919882

Observation 8f022f7e-615c-41b3-8c6e-8cb58a4bd802 · outbound

This paper cites A Survey on Employing Large Language Models for Text-to-SQL Tasks.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges A Survey on Employing Large Language Models for Text-to-SQL Tasks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.881368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.881368Z digest=sha256:993b35ebd10cd8046c9912a26334225ce1b7222cd23132a0f0317c300147c4e3

Observation a63130b1-013f-4f70-abe9-13334fb68245 · outbound

This paper cites Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.889548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.889548Z digest=sha256:8fa3224edc13ebcf8265cb4ca88c80a953818374be2a1ab80044a49dad64cde0

Observation 07687ef5-cf9b-49f3-bd7b-c4f7357282e3 · outbound

This paper cites Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.897369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.897369Z digest=sha256:30b515c5eed0a58d476a8853f7cf992b03468ea7d3dc33d2c9086e15e4fe0c1c

Observation 927f6c74-d116-49c9-b9ad-35c880d82515 · outbound

This paper cites Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.903770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.903770Z digest=sha256:312b400827f48e5d8300585adb10d92553fcbfa70f2541bce2cc9653b4018247

Observation 5f0c144d-d464-44dc-9bef-49a0882d9f0f · outbound

This paper cites A Pilot Study for Chinese SQL Semantic Parsing.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges A Pilot Study for Chinese SQL Semantic Parsing

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.910140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.910140Z digest=sha256:d912899a75a376e2599a784d3ec7db6ee47088ae3d57f5de9612c5308e6be75e

Observation c701bfbd-e635-44a3-bc3d-79cb0868e98d · outbound

This paper cites UNITE: A Unified Benchmark for Text-to-SQL Evaluation.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges UNITE: A Unified Benchmark for Text-to-SQL Evaluation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.918238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.918238Z digest=sha256:4ad3c0386513dc0cac47e143d8ecdecdc3d9303a94738942616b3ac0b372d596

Observation b86dc5cb-2723-4e0d-8ee2-0a01278814ff · outbound

This paper cites CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.924170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.924170Z digest=sha256:4154b775b324e0b2989829abde5ad1080a68d746766f9ce8e5a9d3991c82a5e7

Observation 23907aab-503e-4248-a0ec-a0a9af456714 · outbound

This paper cites Sqlnet: Generating structured queries from natural language without reinforcement learning,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Sqlnet: Generating structured queries from natural language without reinforcement learning,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.835572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.929685Z digest=sha256:8908fceeb1f8d8efc1aef2d4aa1e41b227204e26719f1043abab62fcdc466ca9

Observation 01fcfcc8-7285-4640-ba66-edfbb483fdb7 · outbound

This paper cites Typesql: Knowledge-based type-aware neural text-to-sql generation,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Typesql: Knowledge-based type-aware neural text-to-sql generation,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.808964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.934729Z digest=sha256:3e74ae326156e12acb33d68de8bd3587a70836614f97c2fd97819d22047c46d0

Observation d2f41aab-c9eb-410d-84aa-027d39eb273c · outbound

This paper cites Towards complex text-to-sql in cross-domain database with interme- diate representation,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Towards complex text-to-sql in cross-domain database with interme- diate representation,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.786411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.940947Z digest=sha256:8cfb68d3961b2c14c222449c3c331d6d2b73bcf254ef4ada35fdb5ff24525f6c

Observation 3973f48c-b3b5-452e-8c19-41fa60454105 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.762398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.947028Z digest=sha256:496cd8486808aa64e8bd516a06aaae8b9edaea6fb0281db3502e04d2cb1c3a4c

Observation b3a47c91-b927-4071-aa19-2a835303f703 · outbound

This paper cites Medt5sql: a transformers-based large language model for text-to-sql conversion in the healthcare domain,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Medt5sql: a transformers-based large language model for text-to-sql conversion in the healthcare domain,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.741176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.952788Z digest=sha256:d90ac9ab07608e52f50a38027d5de528eced17c57e31397b820573aea96b9751

Observation c9a8c42f-9aa2-4ba2-810a-6e83818f47d9 · outbound

This paper cites A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.958019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.958019Z digest=sha256:36771549b5ec78f862641734c05cdf2ed674c55321482179b2d26c9b0f14d08f

Observation c66ee645-ffba-481b-9c7d-83946a912272 · outbound

This paper cites Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.716422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:21.963786Z digest=sha256:3c706df0dc01f5f0ba1cbf9a9345e2c274c09fc3d7991ddd7521ee433b0631ee

Observation a711f2fd-2685-478d-8ddd-75392cb9608d · outbound

This paper cites X-SQL: reinforce schema representation with context.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges X-SQL: reinforce schema representation with context

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.975776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.975776Z digest=sha256:2f1789e725b3ceb8fccb24195a0559a6695fcfaa6d6026bfdf3a5dd239373b38

Observation c91c7bde-a9c8-4203-84a2-31d61cd5801e · outbound

This paper cites Ehrsql: A practical text-to-sql benchmark for electronic health records,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Ehrsql: A practical text-to-sql benchmark for electronic health records,

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.981259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.981259Z digest=sha256:5949bc88465ef131c3314977b4ae55e9bd34009db02051e059a45f63ec45d30f

Observation 068de9f1-6f4a-44ec-914f-3bee49bd2907 · outbound

This paper cites RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.989676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.989676Z digest=sha256:a5d44c55322d2442b7ba5210d4247123b34b5df229692d193dba7efcf41d2a1e

Observation 9930bf87-7d37-4f65-ae1e-642c2c85fd38 · outbound

This paper cites PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.995918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.995918Z digest=sha256:d4a11009ae2a7fb9af7a88b2c6b3cb650d99247f3a9b2e4459dc28105dd3eb78

Observation 388a08b8-ab38-4155-a2dc-89748ab3e6ce · outbound

This paper cites ETM: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges ETM: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:22.004529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:22.004529Z digest=sha256:360dd5fe77d578e1462ffa0016b1d2c930735ddc5735f68212f89a4e61942c29

Observation 0ab5f251-fd6f-4e27-b44d-67202c48afed · outbound

This paper cites Practical text-to-sql for data analytics,.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges Practical text-to-sql for data analytics,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:52:22.695437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T20:52:22.011363Z digest=sha256:cec3f2c3f086478c19d2ea7514b95e5bf516796255fc4bad18a243d5d79d90d7

Observation 1a6df158-7f44-40a8-bb97-46163dda6620 · outbound

This paper cites UQE: A Query Engine for Unstructured Databases.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges UQE: A Query Engine for Unstructured Databases

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:22.017799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:22.017799Z digest=sha256:048279fcc3999ea318dc81029f916fdf37db43710914e88fb79c5d3f7cd1387a

Observation 674ffcdd-aca4-4c69-8fb1-ae2609774db1 · outbound

This paper cites RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers.

A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-11T20:52:21.969411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:52:21.969411Z digest=sha256:dce28c1cdce9c89a7b42d86939b22e6f6cc812346ab9eab67b15271125ca2455

Pith citing papers

Observation 743eb14d-5f02-4516-adc2-bd692b52f650 · inbound

CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment cites this paper.

CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:36:45.327275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-28T06:49:23.432383Z digest=sha256:23e99f2c190d62276f310bb798363b098cbaca5978f770fcc04f8df5f9a76ddc