Paper Citation Record · LEDGER

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.07423.

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

pith.paper-citation-record.v1
2506.07423 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:38:16.438426Z

measured 40 of 40 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy11
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a15fa4b-a223-44c1-a142-83cdbb1ab41d · outbound

This paper cites A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.274292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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 1df1ebd8-571c-498e-bbff-1ef986f682e8 · outbound

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Next-generation database interfaces: A survey of llm-based text-to-sql,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.282748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7711d878-ff5b-4f4c-955f-8dcccb80674d · outbound

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.286614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8a74eb-3fd3-4d74-8ce4-afa39e2f8364 · outbound

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Spider: A large- scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:19.536547Z

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 8362539b-63ed-467f-9e2f-229af9511526 · outbound

This paper cites Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:19.346249Z

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 6d120e40-7221-4f1e-8716-ae7e6a1cd939 · outbound

This paper cites Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:19.129283Z

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 6d631799-fb45-4ee7-ac29-06a54dee4cd3 · outbound

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation CHESS: Contextual Harnessing for Efficient SQL Synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.306896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff16517a-c1e9-4be8-a749-f83a9b4d7cd1 · outbound

This paper cites RSL-SQL: Robust Schema Linking in Text-to-SQL Generation.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.311735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c053738-9916-4c99-92b9-a2763ec99a41 · outbound

This paper cites A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.316282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 593acb4f-a354-40b7-a483-e22e4cc24245 · outbound

This paper cites The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.320221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6e42dbb-61f6-427d-b940-c0a6e9b6a3ef · outbound

This paper cites Purple: Making a large language model a better sql writer,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Purple: Making a large language model a better sql writer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:18.851801Z

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 cbe6b730-d104-4e50-9119-69d8b48fca89 · outbound

This paper cites E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.328132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a35100ae-d37d-48e9-866d-c6bbae8882fc · outbound

This paper cites MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation.

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.

Observation bc7c537a-7807-4a49-9eab-677d07830af3 · outbound

This paper cites Codes: Towards building open-source language models for text-to-sql,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Codes: Towards building open-source language models for text-to-sql,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.335952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09d05fd5-590d-46de-9c45-e29e90e1f59f · outbound

This paper cites Synthesizing text-to-SQL data from weak and strong LLMs,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Synthesizing text-to-SQL data from weak and strong LLMs,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:18.633567Z

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 cbfd1ac9-f21d-4264-81d7-943d24345190 · outbound

This paper cites Available: https://doi.org/10.1145/3654930.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Available: https://doi.org/10.1145/3654930

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.339448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13a84b38-18b6-4274-92d5-e19cd47ec97c · outbound

This paper cites Nalir: an interactive natural language interface for querying relational databases,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Nalir: an interactive natural language interface for querying relational databases,

Reference 18

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:38:16.814638Z

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 64c49a07-1de3-423a-b55e-7cd6095fb74f · outbound

This paper cites The dawn of natural language to sql: Are we fully ready?.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation The dawn of natural language to sql: Are we fully ready?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.346179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9def7839-aae2-4640-9b7e-92f3b0a9d8d8 · outbound

This paper cites Attention is all you need,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Attention is all you need,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.357212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a1a96a0-89b4-487b-8582-89f01d9fe848 · outbound

This paper cites Sequence to Sequence Learning with Neural Networks.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Sequence to Sequence Learning with Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.353724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8860740f-9af9-4dfc-8792-6e01ccb45787 · outbound

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:18.140657Z

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 51ab76d5-da45-4edf-a0e2-ce8f1610fc22 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understanding,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Bert: Pre- training of deep bidirectional transformers for language understanding,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:18.405116Z

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 42fb86a9-18c2-4e98-a789-7261603716ca · outbound

This paper cites Get to the point: Summarization with pointer-generator networks,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Get to the point: Summarization with pointer-generator networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.371060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e8fdd62-6779-482c-8280-11b63537667b · outbound

This paper cites Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:17.924960Z

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 38803d7f-081d-42f4-a233-23662f3b0462 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Gemini: A Family of Highly Capable Multimodal Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.378630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7030597-3a6b-4ee2-8441-ddf457f0de20 · outbound

This paper cites GPT-4 Technical Report.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation GPT-4 Technical Report

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.375170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07eca7a3-3e5f-4520-bcb6-77c94f8b1025 · outbound

This paper cites StarCoder: may the source be with you!.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation StarCoder: may the source be with you!

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.385819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bab37083-509a-46c4-beda-83b3bfe0c2ea · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.382341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04b62657-2fee-4135-ab7b-463399279ce3 · outbound

This paper cites Din-sql: Decomposed in-context learning of text-to-sql with self-correction,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Din-sql: Decomposed in-context learning of text-to-sql with self-correction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:17.724623Z

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 66a578c5-bc6c-4531-bc5f-7bd230b84fad · outbound

This paper cites C3: Zero-shot Text-to-SQL with ChatGPT.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation C3: Zero-shot Text-to-SQL with ChatGPT

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.391072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41deecea-15a6-44f8-a299-b6b475bb3fbe · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.405275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 851bbb25-657b-4ca5-b2d7-5391539de85f · outbound

This paper cites Text- to-sql empowered by large language models: A benchmark evaluation,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Text- to-sql empowered by large language models: A benchmark evaluation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:17.506198Z

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 49dfb382-4cfd-4f29-9072-ca9d5cfbf21a · outbound

This paper cites Self-Polish: Enhance reasoning in large language models via problem refinement,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Self-Polish: Enhance reasoning in large language models via problem refinement,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:17.295601Z

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 dc6c4376-71d4-4bae-8f5d-7b83d069dbd6 · outbound

This paper cites MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.409851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caa024c6-2715-4db4-8d32-0e71b55a6f50 · outbound

This paper cites Mpnet: Masked and permuted pre-training for language understanding,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Mpnet: Masked and permuted pre-training for language understanding,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.427700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be8a2323-05e6-4328-bd2c-b73ce849ac6a · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.422463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1141bbed-6572-4c8b-b0d9-38afe4c01426 · outbound

This paper cites DeepSeek-V3 Technical Report.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation DeepSeek-V3 Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.438426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 652f7266-0240-4d06-9dd5-85bb2dd06869 · outbound

This paper cites Resdsql: decoupling schema linking and skeleton parsing for text-to-sql,.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation Resdsql: decoupling schema linking and skeleton parsing for text-to-sql,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.433061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b9e1aaf-b702-4f55-815b-6713b985dc00 · outbound

This paper cites CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:16.302160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.