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Paper Citation Record · LEDGER

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

As of 12 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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:10.929888Z digest=sha256:1f957797e1591f8447be22ef0a26f06e58dbdf487712f569a81ad520ccf590af

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=sha256:3ac84f7f3dc5f321bd312e224fa32d56c4f0fb856f44625cf12b552f63b33e4f

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:10.942722Z digest=sha256:a7ad82eeb9f3a4d457cac912d1d2876d9fc353e6ef77c063b393deb7ba1e1ab3

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.059530Z digest=sha256:6bbeb036ebbe08b073f6709f90fe1dee4ed0aa26744f26579aa08fd9a2c32252

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=sha256:38ab38875a20cd548be61bece1fc3b42469d49432ca382863874b2db3106b5f1

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=sha256:1f61f67186850c5b034a5f56656710574b8201ac37f0ddcced0cc39301e211e2

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
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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=sha256:7bd4fc7b9ea5ef239274bb25ed0c4674f655e07c6b25a674f03421dfb888dd2b

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=sha256:249a6403d702d489b23f0de0a2e03af0f6c3e53fd0ff5f43e1f8144946dc5322

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.075619Z digest=sha256:7ae019222c65301b3de049b7adb139965114ef2d63d8b5cee9f258290a21188b

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
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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:eac9aaf56831380682a41797b75ecd11b9ba79b1316f045bca7469b7ac73d8a6

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:e72fe70d4a1c9f576b9f431fcf21dc903d5bf86c66c29f17d5675c73344a8881

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:a413859e6ce7af8ee1d01528ea2e28c87f1452c83567be8af09ec6efc48fbd46

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=sha256:358df86d1b24e82b535d9f8d08bae06dccbddd4a95aac0bc5cbeb454f9c55c78

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:a29f672ff8af096f30c08838ac9b0a5339440647ceb44b96ce5218416deeb2bc

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.100843Z digest=sha256:7412446d083326d1bb31747d0828c14fd77481ae58397451ca963dc737725a37

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=sha256:91e79b68bd7dea6bb13960b281c8cded4a2e9b3589b244738e330717a9d38f23

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=sha256:91eaddb828643d09aac47e43552d87435462dfe285c488148a55a939a1190bbc

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.119985Z digest=sha256:81e0e243d529dae828964089c0128682d7c812ffca7be545381dd1a87e07c335

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=sha256:8b537418bdd2d0fd947dca78e5d8cffa1f0b9f1a0f45e77a2ad7f73bad8dd690

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=sha256:4a70f828df8c3c469582e0f091085d2832e8b25287c82f1d65ec7e207f801fa7

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=sha256:02c1a42013e60af89d7ea6d2b8975bee6b4a135abf4e90113c4db87496415e88

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.135664Z digest=sha256:5e20776652ea939208e3914239052a8a1f90d67dcf03b2c316d5d3ae52e337a1

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.131828Z digest=sha256:e3a778ad403ee20a9a0f5231a7366df720622cd61de213c69b5eb32b8b5bb5e5

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
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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=sha256:8618fec54f84027e3ae34421afb4b2a4266d653796734d97537139be0e42cbfc

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.139455Z digest=sha256:d256d710bd00e549dec69cc146d78ef0132cd509680276c57a91f276772b2675

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

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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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.151392Z digest=sha256:7666b28777cb6f8fa816fba44dfbe2b178f6398c21570a1945a26a997c040b7b

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=sha256:3519a59f904770e76e8e0f9034ab06c88aba35c6ac5732412980cace3dd730f0

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=sha256:342990b985d28340ae80f859afdf1614c84fb8bc9434de87a1fb9368aec31a9a

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.155280Z digest=sha256:af8878c82e981fee89158345d7c7cfa4b8edccecaf70a2270d9c2ec6e97dad27

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

Resolution
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=sha256:2313535edd07f46c4fbac3688e5b9b84a281341060c358aaac6070445ea211e7

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:ab41870ef502ffa47c02d3f1935555808435319814e07078a92a20dfa7324983

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=sha256:219d94c5b76d1f9358c61b4738580885994a9fb552564e3f5930a2116120e8cb

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.170683Z digest=sha256:924b15e09c7a29e60dbdd3cb54b0ba0017cadc932f4e7320627989715d74001a

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:bf7ed906c927e02bd590b7234ccae5576b74f795b25b9779faeb2538d75cfb49

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.178070Z digest=sha256:1ac1b168298cf9ab15712cbfbe8fb1d4915bb5d85d4227f257622498521e9105

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=sha256:31e76671840671bedc28153149933e2fb47bf8be926bee66fda3ef9833fd9139

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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no resolver link, observed 2026-08-11T10:32:11.197816Z

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source=pdf_text observed=2026-08-11T10:32:11.197816Z digest=sha256:3c29a1066e6e713d23ef04301e263568b5320d87335037798885790bd4bb1061

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.189883Z digest=sha256:b7cb59e1a8e85041d0ccae06dbc8719e73c2e188cff3889df9cedf76a2aea965

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=sha256:61f8d89cbc72d2f38e1413faa368f55a085ab494fa77820048a0bb6a4cbd839c

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=sha256:70f93865ea7a60f98503271d7e0e2e2011ce3c1dfa54833ba63ab36ca2fc52f0

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.201396Z digest=sha256:6b3c16697f227adb2915e40c30fc307f1b4c4c584a7056f2fa197da2cc78cb3e

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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unresolved
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=sha256:8f3191181753c874543f0c9b43d6822cbee4a527404314100d6c50996dff308b

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:32:11.225711Z digest=sha256:4847e1481291f3f30757d573bd84f2a987b7ea24c5c2c81c6ae6e1435b2e55d1

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.229466Z digest=sha256:49afe7243b765dfccc17fa04e3ccc426a784ae3570ae3c8717ec8c23c62a3e35

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=sha256:4784d9fb6bced0b7a0656b5dbcb275f86aab2064196e815022987cd84edd584d

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:11.222057Z digest=sha256:d97452b26f93feba3b213d63d779853ab4444991b289f5ff2b39a7e89b88cdde

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:bf88fc2781034e3afa72ffd7329844ff5c7e158e8fdb7838cc4b6f29d65aadcc

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T10:32:10.934218Z digest=sha256:17ad92773894e0cfaaa7df7aca373708fbb27be4ae34c513dedbfaa3354882bd

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-29T21:12:50.656479Z digest=sha256:cd48c0c71059847e8e7f071db102ffa6726bed4490c58451f28168afb04f1135