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Paper Citation Record · LEDGER

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2412.17867.

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

pith.paper-citation-record.v1
2412.17867 v4

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:32:11.229466Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T21:12:50.656479Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T21:13:59.761947Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact2
  • verified fuzzy15
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6edd36b5-49fa-4238-bf2d-d67725cef1fd · outbound

This paper cites Interactive-t2s: Multi-turn interactions for text-to-sql with large language models,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Interactive-t2s: Multi-turn interactions for text-to-sql with large language models,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.889038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:10.929888Z digest=sha256:226e1db6f7c0f54de78e1dcccca4896f8bef86828db0f6edc48b1c0ad5567893

Observation dafff2ac-0050-4229-b6fc-964a2f5775de · outbound

This paper cites ChatBI: Towards Natural Language to Complex Business Intelligence SQL.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types ChatBI: Towards Natural Language to Complex Business Intelligence SQL

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:10.938089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:10.938089Z digest=sha256:214fe61cc37a4c0c9b0c6c56bd01ee7a09bb002eb218f79b82c6c3cc3b4bb41a

Observation be83377e-ee26-45d8-aade-31e845c1a808 · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.874920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:10.942722Z digest=sha256:79dbfd4c149acddd0eb96b2b59e0e9a66af133733c9fc45b6c4f4e955398932c

Observation ffacd9a9-a55f-4fa9-8e6f-342dafe75358 · outbound

This paper cites Conda: state-based data augmentation for context-dependent text-to-sql,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Conda: state-based data augmentation for context-dependent text-to-sql,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.862398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.059530Z digest=sha256:cc413d13184a07515545587ab44782ca4946ba3b96375961a02d956b797f503e

Observation 5715aa5e-5626-4614-a331-936ef78b385f · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.063229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.063229Z digest=sha256:9964bbf5d352bbc2465440f8d033f41051274d549df5f14fa3a8075d8efa9d0f

Observation c6006cc3-ff4c-4f62-8f63-c878bb655e4b · outbound

This paper cites MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.071443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.071443Z digest=sha256:02fa2ba0e6372ddb8af738b8878d538828ec8f994c5b9fa7efa151e8961e6c27

Observation 09a48938-2603-4daf-a503-f217c564108c · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.067181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.067181Z digest=sha256:534322fda6844950a16b3845bfed337679b70bbc2ef1872d6a4065af229d296d

Observation 1ee88942-7752-47a9-85e7-bd849e899f61 · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types A Survey of Hallucination in Large Foundation Models

Reference 8

Resolution
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no resolver link, observed 2026-08-11T10:32:11.079401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.079401Z digest=sha256:a268b6ed3a5fb70fa2aba6026e621093b53057dd21bd1292707d029965697020

Observation c9ba131b-6f8f-4fe1-a311-ae2357f961e8 · outbound

This paper cites Know what i don’t know: Handling ambiguous and unknown questions for text-to-sql,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Know what i don’t know: Handling ambiguous and unknown questions for text-to-sql,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.840844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.075619Z digest=sha256:82439bcbe6607481f1f51b62f0f4c31bc3aca64af0a5a8b2e43ae9e8c66cff21

Observation 153408e1-673a-4b8f-861f-539a3bbe89db · outbound

This paper cites TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoring.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoring

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.088269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.088269Z digest=sha256:4d0f31edb34f0ec4baf823282ed4eeeddcee6c041af8c204fad5b9896299e3b9

Observation b701b11c-6821-4ac6-8a89-c84bdcd219b7 · outbound

This paper cites AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.083905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.083905Z digest=sha256:1e5a0c8b5d2302b5ada35e8e091e35e64893177dd2c71ce30cdb8773d987a2f3

Observation e4b24acc-e072-4fba-af75-f6a436105fe2 · outbound

This paper cites SParC: Cross-Domain Semantic Parsing in Context.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types SParC: Cross-Domain Semantic Parsing in Context

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.096643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.096643Z digest=sha256:540e8281473fef12f8b6579d368343e55429473bcd7645418fd83464ef2db672

Observation 19a463f9-1b65-4da7-9903-0ba82f6d9fbe · outbound

This paper cites Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.092666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.092666Z digest=sha256:f6606a2acfac1effd027e8cdf04b643930f62b3932a4bb918fb710ee1ffc0d7a

Observation 65c64fcb-5c9f-4b58-9f17-8e93f0370238 · outbound

This paper cites Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.104805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.104805Z digest=sha256:83ee469f9f86126b9a4d1da1d11724f33cda4bc51fbb4d91850746584f8db26a

Observation 3cd0f214-99d2-4bad-ac3a-2539f6185002 · outbound

This paper cites Chase: A large-scale and pragmatic chinese dataset for cross-database context-dependent text-to-sql,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Chase: A large-scale and pragmatic chinese dataset for cross-database context-dependent text-to-sql,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.826520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.100843Z digest=sha256:a7a23c556551f33ee8edb1dc6545562ef2c7312be5ad291e5adf16457331e0eb

Observation 2e27203f-ec7b-4af4-a556-cb67e9847d42 · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.112652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.112652Z digest=sha256:cdf394e9de6ccec919e3b3928047bb66fe7c0052eaf73f936ad3efdfc7c6d829

Observation 5f5e7013-bf78-4f74-af5d-57673a73cda5 · outbound

This paper cites QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.108989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.108989Z digest=sha256:1471261b547808f22ed428216872d70d8b8a06ceeb3441f92de240150a3b1422

Observation da21000b-4d12-4c1c-be3a-089e4aceaf2b · outbound

This paper cites CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.814339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.119985Z digest=sha256:f8e3881bf65d68f3e3a6a9ead40d94d3351754b785ee93004c4c15873d2e8992

Observation fd0c3d7a-40db-4dbe-ba9b-530692741328 · outbound

This paper cites Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.116179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.116179Z digest=sha256:d9aa7fe714cf5963ea355eff163b1d2210d2467540c28da061b1619cb33e2497

Observation 649bba4b-abb1-4552-9ffd-cf22ab08edb1 · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Reference 20

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unresolved
no resolver link, observed 2026-08-11T10:32:11.127906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.127906Z digest=sha256:788ac363e3ba8b98273a435cd9cc468dfa64b54c1bc13fa7896e27bbcbf52a84

Observation 65ea5e78-64bf-4caa-a35d-7c9dfc5c0f19 · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.123619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.123619Z digest=sha256:7303a7c66b3304899e387725ecaacb4d90c6bf5d5a8660cd497b3ec8cc527802

Observation 19096444-5489-476c-8dc1-0e13c4b57e86 · outbound

This paper cites Text-to-sql with large language models: Exploring the promise and pitfalls,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Text-to-sql with large language models: Exploring the promise and pitfalls,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.790440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.135664Z digest=sha256:a023ccfe900a1d07d6a79d4cf6cc4a252e02fe12406a51c513f6c1ce96e4073d

Observation 54ecb1a4-a7e5-43e9-9b9c-cb53fb135d38 · outbound

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types RAT-SQL: Relation-aware schema encoding and linking for text- to-SQL parsers,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.802849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.131828Z digest=sha256:9be0534bdf6afc5ad1f1d55339646db059b3f266ffbbf0642c774cce69a47a23

Observation 681b221e-6765-40da-b800-e2699e49619c · outbound

This paper cites DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.143103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.143103Z digest=sha256:15844171bf9bac3c7a6e76fddbb6cd8f14d2a16bc8de2b955c87f780472f4be9

Observation 79c00550-b824-4b2d-8eb8-861326522718 · outbound

This paper cites Optimization modeling and verification from problem specifications using a multi-agent multi-stage llm framework,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Optimization modeling and verification from problem specifications using a multi-agent multi-stage llm framework,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.776694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.139455Z digest=sha256:fe53872a4f65a61921b2498b22fde770f2a86e2eb9d7cde01fdd16c81e87722f

Observation 10210bca-fe37-4779-844d-0232a45c228f · outbound

This paper cites Evaluating text-to-sql model failures on real-world data,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Evaluating text-to-sql model failures on real-world data,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.754532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.151392Z digest=sha256:b05f9a709170ba528de164d5a38fc3810463e41dd7d786356ce23ad8038a0490

Observation 1cfba163-291b-4e24-942c-702b7b15c302 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Chain-of-thought prompting elicits reasoning in large language models,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.147284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.147284Z digest=sha256:ebb39689784fe8350c775cb132f21060d6a53bafc63856f8e213c60a2c8f7ff6

Observation 492b6229-35be-4b58-bcf6-46d38ca73a02 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T10:32:11.158928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.158928Z digest=sha256:58a7276ade8f1c3b5812690f8fafff63b13b036cc231e6b9897c0744c15ed05a

Observation 42dea213-9517-4b60-80c6-3bc90f8f0481 · outbound

This paper cites Benchmarking and improving text-to-SQL generation under ambiguity,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Benchmarking and improving text-to-SQL generation under ambiguity,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.741677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.155280Z digest=sha256:40aade73d443e603856e4cfc10f354abebe1dca07f16b34938e160b0086b8280

Observation 664b2fca-5e4c-4559-9380-e64c57645539 · outbound

This paper cites A survey on large language model based autonomous agents,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types A survey on large language model based autonomous agents,

Reference 30

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unresolved
no resolver link, observed 2026-08-11T10:32:11.166941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.166941Z digest=sha256:fb62962c7ab3ff4acd44f0398c062671df059baeefe7fdaa0071b39c52d49cb6

Observation a57c21eb-c883-4279-8326-624b6feec07b · outbound

This paper cites Cognitive Mirage: A Review of Hallucinations in Large Language Models.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Cognitive Mirage: A Review of Hallucinations in Large Language Models

Reference 31

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unresolved
no resolver link, observed 2026-08-11T10:32:11.162911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.162911Z digest=sha256:e798c426e04c3ee089081ebb7b4e513e8397d7e9535f3c0de47f70302d9cddd6

Observation ea030616-c0ea-4ce1-957b-51c491a40daf · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 32

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unresolved
no resolver link, observed 2026-08-11T10:32:11.174316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.174316Z digest=sha256:2503d7051f9491caf64cd85e140f0144378ec05c19016a6ebcbf47519add9ff5

Observation b076ca3a-c648-4010-b755-399d465b91a3 · outbound

This paper cites AutoGPT,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types AutoGPT,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.721295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.170683Z digest=sha256:dbf2a7d7938299ff5a176d38dc2a7f7cd55d0639da27623987c1d4442e326f2d

Observation 5debf461-8c6a-4866-a27d-6d8c35b4ef3f · outbound

This paper cites Instruction-following evaluation for large language models,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Instruction-following evaluation for large language models,

Reference 34

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no resolver link, observed 2026-08-11T10:32:11.182101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.182101Z digest=sha256:eaca93b729395ec51479f11e999653caa2772681b9ad839491d037493ab4fb6e

Observation 2bbaa214-2503-46c2-8f15-e2790912c335 · outbound

This paper cites MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL

Reference 35

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source=pdf_text observed=2026-08-11T10:32:11.178070Z digest=sha256:d16003f0b4d166dccd7c7637f58912f222bb1817421cb319c23cd54e8819ce90

Observation dd26b6f7-8aad-4975-9346-9dd74318f6ac · outbound

This paper cites MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

Reference 36

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source=pdf_text observed=2026-08-11T10:32:11.193825Z digest=sha256:95a617fec746e8c64ea7c32a070ae356a13eac0e72d48ae2b06d7e9e7a5ff922

Observation 4fdd8091-b7c0-436a-b5e5-18852752b6c5 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 37

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source=pdf_text observed=2026-08-11T10:32:11.197816Z digest=sha256:447393dadf1d362f5af1b18d97c4977aab1e81173843bb15c9bc49eed5f79a31

Observation c819827b-fcce-4637-a429-99a051c0e625 · outbound

This paper cites G-eval: NLG evaluation using gpt-4 with better human alignment,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types G-eval: NLG evaluation using gpt-4 with better human alignment,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.701474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.189883Z digest=sha256:ab8edf5d39df6c38cd2812d940099ebe9c5d1e8a477e8da2593478faa8c5ee77

Observation 5dbcaf13-11b2-4ff4-bcad-df69789b6f5b · outbound

This paper cites ChatDev: Communicative Agents for Software Development.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types ChatDev: Communicative Agents for Software Development

Reference 39

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source=pdf_text observed=2026-08-11T10:32:11.209508Z digest=sha256:2fa333d97a7717f46dc1d470bf3667f18ceaee9d2d2fe4a3cdd75d6ed2636d32

Observation 5decd463-58d4-4470-ac97-41a68c996cae · outbound

This paper cites LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods

Reference 40

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source=pdf_text observed=2026-08-11T10:32:11.213935Z digest=sha256:a20e4870d95725a6d4f6596379f5873d199781e09d2939ad9cd29efb0299ce73

Observation a1b817a8-9e1f-4e7f-803a-32a7a5d68702 · outbound

This paper cites Large language models are not fair evaluators,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Large language models are not fair evaluators,

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.680534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.201396Z digest=sha256:d9e40c2a162c83950683d01984abb77f5f0e73094cacd4f8f2feff21269cde26

Observation 466c1153-1c28-4f5b-b24d-61bed5d0b83c · outbound

This paper cites Large Language Models are not Fair Evaluators.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Large Language Models are not Fair Evaluators

Reference 42

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no resolver link, observed 2026-08-11T10:32:11.205236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.205236Z digest=sha256:93f1222920c3e4d0a48f18507d6097472b6330389c33c548b3effddf4b306ebd

Observation 106b235f-74cb-4f35-b2a5-35c689ba378a · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.225711Z digest=sha256:3c6ac9943cbaf89674beab0f5cf6ac695767bfcb4e95d657aa9612d314673d70

Observation 0eac8988-03ba-4edf-856a-2ed1030db9b7 · outbound

This paper cites Grade Score: Quantifying LLM Performance in Option Selection.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Grade Score: Quantifying LLM Performance in Option Selection

Reference 44

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verified exact
local_arxiv, observed 2026-08-11T10:32:11.267686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.229466Z digest=sha256:775b2666f41d1430f1798d57952127dee5c0e0b02f087c2f43ad30f5087ed6f1

Observation 804144bd-94f1-42aa-8046-2718c12745ba · outbound

This paper cites Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities

Reference 45

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source=pdf_text observed=2026-08-11T10:32:11.218017Z digest=sha256:851ecfe78d342dd0e7202ffabbe2d28c568e5ceed55a101b07f29a12943a8a63

Observation ecee97c7-898b-47c1-97a7-457dc5c72a6f · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 46

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verified fuzzy
raw_fallback, observed 2026-08-11T10:32:11.666994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:11.222057Z digest=sha256:05305725b11608fcbf574463c9a86e265f86edb2979d4f1ca2ac72b8bb7fab80

Observation 8a0f0e64-a8f2-4df2-a03b-27c121b2c22c · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Instruction-Following Evaluation for Large Language Models

Reference 2023

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no resolver link, observed 2026-08-11T10:32:11.185581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.185581Z digest=sha256:2a194d711a4da9614b51177c7583ced54416661e2a06e2eded45de3062e15e12

Observation 5057d25f-1cbc-4519-9dbe-1c43e355e201 · outbound

This paper cites Available: https://arxiv.org/abs/2408.11062.

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types Available: https://arxiv.org/abs/2408.11062

Reference 2024

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verified exact
raw_fallback, observed 2026-08-11T10:32:11.655422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T10:32:10.934218Z digest=sha256:634bdb039615cd7cd36713e9c53745a2c35c9b9dc34f1a23eeaf1d06777f1f67

Pith citing papers

Observation 72372bde-4f4e-4948-9db2-07daf01f285b · inbound

Memory Architectures for Multi-Turn Text-to-SQL: A Benchmark and Empirical Study cites this paper.

Memory Architectures for Multi-Turn Text-to-SQL: A Benchmark and Empirical Study Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

Reference 1

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verified exact
arxiv_id, observed 2026-06-29T21:13:59.763229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-29T21:12:50.656479Z digest=sha256:d7b4296ab9bbbf7a9d3c56e894db6c118e695d1026bc05dfcb413506c2e938e5