Paper Citation Record · LEDGER

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 100 inbound Pith citation observations for arXiv:1709.00103.

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

pith.paper-citation-record.v1
1709.00103 v7

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-13T16:58:50.222770Z

measured 143 of 143 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 100 of 154 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:54:52.683948Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy37
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

788
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 56e411c9-69a0-4611-9034-a3d61cdeb834 · outbound

This paper cites Androutsopoulos, G.D.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Androutsopoulos, G.D

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.325380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5eae29ad-c762-4449-9ccc-4c7df42e8245 · outbound

This paper cites Zettlemoyer.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Zettlemoyer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.329723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 377981bc-3c5d-440b-ba2b-07b16acdd3d1 · outbound

This paper cites Zettlemoyer.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Zettlemoyer

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.247747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 09de9900-8793-474b-9da9-89e305969407 · outbound

This paper cites A new database on the structure and development of the financial sector.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning A new database on the structure and development of the financial sector

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.250238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ccf0486-28f9-4e75-ab43-c534dee5aa5e · outbound

This paper cites Le, Mohammad Norouzi, and Samy Bengio.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Le, Mohammad Norouzi, and Samy Bengio

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.252555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec0fe5bb-2db3-4eb3-b761-9492c9b1b17b · outbound

This paper cites Semantic parsing on freebase from question-answer pairs.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Semantic parsing on freebase from question-answer pairs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.254875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e39a415-c795-4aeb-bd40-846a39fe7bfe · outbound

This paper cites Methods for exploring and mining tables on wikipedia.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Methods for exploring and mining tables on wikipedia

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.257829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9e9ba818-451e-4bab-907b-5b94317d6c37 · outbound

This paper cites Large-scale semantic parsing via schema matching and lexicon extension.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Large-scale semantic parsing via schema matching and lexicon extension

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.260578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e60aee59-5c29-4f5e-beaa-a8a725bc730c · outbound

This paper cites Language to logical form with neural attention.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Language to logical form with neural attention

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.263644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f39de98-97c2-4c46-a517-bb91503bdcba · outbound

This paper cites Translating questions to SQL queries with generative parsers discriminatively reranked.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Translating questions to SQL queries with generative parsers discriminatively reranked

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.266433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ad3886d-6fcb-4a4e-b800-f4f8f533a865 · outbound

This paper cites an unresolved cited work.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-13T16:58:50.268879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ed976ee-aa59-441c-82cf-991f6026008b · outbound

This paper cites From language to programs: Bridging reinforcement learning and maximum marginal likelihood.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning From language to programs: Bridging reinforcement learning and maximum marginal likelihood

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.271359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4c84e94-be37-4658-9326-9b6b5c30a412 · outbound

This paper cites A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.240564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b5773ea2-c446-4534-a08d-31ec46229bc8 · outbound

This paper cites Can electronic medical record systems transform health care? potential health benefits, savings, and costs.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Can electronic medical record systems transform health care? potential health benefits, savings, and costs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.273647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8e6e1fd-5bb8-428e-9b59-f1510256f641 · outbound

This paper cites Long short-term memory.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Long short-term memory

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.275438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ee113830-9946-46f2-a8ab-987592226df1 · outbound

This paper cites Learning a neural semantic parser from user feedback.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Learning a neural semantic parser from user feedback

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.277513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9e15def-cde4-4f72-89b2-4250d959d74e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-13T16:58:50.244578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2bda722-bb16-4383-93f1-925300859eed · outbound

This paper cites Le, Ken Forbus, and Ni Lao.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Le, Ken Forbus, and Ni Lao

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.279487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17bb4377-7dec-421a-a6f5-8f9e7fe6fd52 · outbound

This paper cites Jordan, and Dan Klein.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Jordan, and Dan Klein

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.281458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74e8d417-4378-498a-b832-273756e0fce2 · outbound

This paper cites Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.283376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94508474-1c1e-4062-8dd0-37801423b44b · outbound

This paper cites Pointer sentinel mixture models.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Pointer sentinel mixture models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.285413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6d90d6ee-3f6c-44c5-ba36-a5fa7b974dec · outbound

This paper cites Coupling distributed and symbolic execution for natural language queries.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Coupling distributed and symbolic execution for natural language queries

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.287251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8cf479ec-747b-49ae-827b-7937e490df95 · outbound

This paper cites Le, Mart \'i n Abadi, Andrew McCallum, and Dario Amodei.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Le, Mart \'i n Abadi, Andrew McCallum, and Dario Amodei

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.289027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a09e133-a207-4ce8-b050-169ff1054a03 · outbound

This paper cites Application of data mining techniques in customer relationship management: A literature review and classification.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Application of data mining techniques in customer relationship management: A literature review and classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.291104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f1b6687-450c-45f1-99d5-4e1bf98f81e8 · outbound

This paper cites Compositional semantic parsing on semi-structured tables.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Compositional semantic parsing on semi-structured tables

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.292956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 630bc72c-f45c-415e-a109-7cd25d670bfc · outbound

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Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-13T16:58:50.295133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2cdaac58-bc07-4012-8d35-2b59c2d0ce5f · outbound

This paper cites Towards a theory of natural language interfaces to databases.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Towards a theory of natural language interfaces to databases

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.296925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9cb8d310-eae8-4aa3-ba77-16469923f1b9 · outbound

This paper cites an unresolved cited work.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-13T16:58:50.298599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6a320e2-99e6-4372-94aa-4f259987ade3 · outbound

This paper cites Large-scale semantic parsing without question-answer pairs.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Large-scale semantic parsing without question-answer pairs

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.300651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eede0e78-6f95-4459-b23e-5865691223a5 · outbound

This paper cites Gradient estimation using stochastic computation graphs.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Gradient estimation using stochastic computation graphs

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.302461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c81f64df-3d0a-4b80-a848-f4c6479a2839 · outbound

This paper cites Bidirectional attention flow for machine comprehension.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Bidirectional attention flow for machine comprehension

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.304169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1522432a-5f42-475d-b16a-034a6f09668f · outbound

This paper cites Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.306225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8783d4ec-518b-481e-afec-dd70d3470137 · outbound

This paper cites Joint learning of ontology and semantic parser from text.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Joint learning of ontology and semantic parser from text

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.308040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f57ec72a-9920-4ebc-8d10-49377c4c19b1 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Policy gradient methods for reinforcement learning with function approximation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.310043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation adfa62c8-0328-44ff-b8dd-46c02e396993 · outbound

This paper cites Tang and Raymond J.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Tang and Raymond J

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.311748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a770d678-44c3-4dbe-8d3e-e4eccbc7b5d8 · outbound

This paper cites Pointer networks.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Pointer networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.313462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6bea7f66-f691-433a-8f7b-75e01426b172 · outbound

This paper cites Building a semantic parser overnight.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Building a semantic parser overnight

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.315228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ebabd61-749d-4389-8be2-9feaef6d6509 · outbound

This paper cites an unresolved cited work.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-13T16:58:50.316870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 268b9df9-a3cb-4620-ae9d-4a3140ef13fc · outbound

This paper cites Dynamic coattention networks for question answering.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Dynamic coattention networks for question answering

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.318599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72f86c20-7d4e-451b-a395-41383936a7af · outbound

This paper cites Neural enquirer: Learning to query tables.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Neural enquirer: Learning to query tables

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.320385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cfcf7085-fd93-4b6f-ac95-74a478166144 · outbound

This paper cites Zelle and Raymond J.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Zelle and Raymond J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.322054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d622c421-e19e-460b-b395-c2f56130f5db · outbound

This paper cites Zettlemoyer and Michael Collins.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Zettlemoyer and Michael Collins

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.323737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8e2ef0af-5fd3-4879-99b4-5b39b77b553e · outbound

This paper cites Zettlemoyer and Michael Collins.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning Zettlemoyer and Michael Collins

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T16:58:50.327084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 6248213b-e687-47bc-bb1e-f515a903b6cd · inbound

Neural Machine Translating from Natural Language to SPARQL cites this paper.

Neural Machine Translating from Natural Language to SPARQL Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T18:41:08.168223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8c3f82d-eefb-4678-abf5-4a489e2e123e · inbound

Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers cites this paper.

Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-25T14:35:56.801588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9dbe77b-7623-4270-970d-6a335337572f · inbound

MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data cites this paper.

MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-25T13:35:53.646694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 78b7f96c-1396-492e-b0ea-4d426fce3033 · inbound

CraftAssist: A Framework for Dialogue-enabled Interactive Agents cites this paper.

CraftAssist: A Framework for Dialogue-enabled Interactive Agents Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-24T19:09:50.159047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d23e7c1-8b7d-4e58-9fe5-2477fff6479f · inbound

Why Build an Assistant in Minecraft? cites this paper.

Why Build an Assistant in Minecraft? Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 103

Resolution: verified exact; local_arxiv, observed 2026-05-24T18:24:48.467188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd65cb3e-57d9-427b-abce-1917f23d42bc · inbound

FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System cites this paper.

FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-14T15:41:50.854475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8daf851d-f838-4a05-b827-3c4f25a8cabe · inbound

A View on Deep Reinforcement Learning in System Optimization cites this paper.

A View on Deep Reinforcement Learning in System Optimization Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T15:21:50.577434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd3ffc67-6f52-4aa4-bf94-3e9d94f667a1 · inbound

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

X-SQL: reinforce schema representation with context Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T11:52:38.417465Z

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Observation 89e91117-889a-4f82-a600-a14f81647dc0 · inbound

Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing cites this paper.

Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-14T11:02:19.930766Z

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Observation da52e575-683b-4b4f-91c3-1ed91cb07919 · inbound

SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension cites this paper.

SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-14T10:36:17.560199Z

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Observation b3461565-bb02-4cd5-8b34-fc7c43a5f770 · inbound

Zero-shot Text-to-SQL Learning with Auxiliary Task cites this paper.

Zero-shot Text-to-SQL Learning with Auxiliary Task Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T10:29:05.009651Z

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Observation af89ec0a-832c-4bca-8c93-3c857578a241 · inbound

Answering Conversational Questions on Structured Data without Logical Forms cites this paper.

Answering Conversational Questions on Structured Data without Logical Forms Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-14T10:10:40.211713Z

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Observation b6e94ad4-6ed3-42bc-bc8c-2163d96993df · inbound

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions cites this paper.

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-14T05:40:13.456870Z

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Observation d019267d-fa63-40a3-99bb-8382db9d76da · inbound

Frameworks for Querying Databases Using Natural Language: A Literature Review cites this paper.

Frameworks for Querying Databases Using Natural Language: A Literature Review Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-14T05:27:15.345268Z

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Observation 55e1e615-7e06-46e5-9a02-ac595fdc3cc2 · inbound

LoRA: Low-Rank Adaptation of Large Language Models cites this paper.

LoRA: Low-Rank Adaptation of Large Language Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

Source-reported events for the cited work

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Observation 48d29aac-b557-46a1-814b-f7515e503994 · inbound

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cites this paper.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 81

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T09:51:46.880280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c45fc6e-485d-40d8-870b-abf8412c31a5 · inbound

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

StarCoder: may the source be with you! Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 181

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e24df412-ebf6-40ed-b793-e8d9b5f498e3 · inbound

TinyStories: How Small Can Language Models Be and Still Speak Coherent English? cites this paper.

TinyStories: How Small Can Language Models Be and Still Speak Coherent English? Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-25T07:36:55.202850Z

Source-reported events for the cited work

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Observation 22ee4869-db3a-4e27-abba-a5255d22051f · inbound

Large Language Models: A Survey cites this paper.

Large Language Models: A Survey Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 186

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

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Observation a5710046-6957-4489-9f68-c22eb08bea8a · inbound

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval cites this paper.

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T17:24:31.631925Z

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Observation 51bcfb48-6f8b-4c8e-8c3e-4edc26f45b93 · inbound

EzSQL: An SQL intermediate representation for improving SQL-to-text Generation cites this paper.

EzSQL: An SQL intermediate representation for improving SQL-to-text Generation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T10:48:09.569403Z

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Observation 18099b91-ca34-460a-bcd4-ca79332c4c28 · inbound

FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data cites this paper.

FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T23:10:13.629672Z

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Observation f5f8b904-6528-44f9-8a07-4ec8b912f616 · inbound

SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction cites this paper.

SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:54.881887Z

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

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

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

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Observation 7eb76f70-636f-440e-8f69-4afdd826f117 · inbound

Infusing Prompts with Syntax and Semantics cites this paper.

Infusing Prompts with Syntax and Semantics Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T20:05:40.727857Z

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Observation d892d1d6-7c63-48d5-8749-6481ea395d30 · inbound

Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models cites this paper.

Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T19:00:13.210745Z

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Observation c1a1e596-dcc4-4269-9615-1efe9222132c · inbound

Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering cites this paper.

Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T18:43:18.983509Z

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Observation bf8f340e-e92d-48a6-8c55-5b2f38c07a30 · inbound

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs cites this paper.

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T17:50:17.713793Z

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Observation 9ec94f23-6607-440b-b990-75bb53669969 · inbound

Obfuscated Activations Bypass LLM Latent-Space Defenses cites this paper.

Obfuscated Activations Bypass LLM Latent-Space Defenses Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 111

Resolution: unresolved; no resolver link, observed 2026-08-11T16:59:11.792119Z

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Observation 5717e210-5145-48f3-b57a-79beb377058d · inbound

RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector cites this paper.

RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:25:36.723954Z

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Observation 9f69b43b-f803-41df-8862-e9dcd6b3d937 · inbound

MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning cites this paper.

MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T14:43:37.134442Z

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Observation d48e876f-6061-43e4-a9d0-0231dab411f5 · inbound

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning cites this paper.

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 133

Resolution: metadata mismatch; local_arxiv, observed 2026-05-17T07:51:13.164937Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4160a583-25d3-4eac-a615-09a5d7cf27d8 · inbound

Bridging the Data Provenance Gap Across Text, Speech and Video cites this paper.

Bridging the Data Provenance Gap Across Text, Speech and Video Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 241

Resolution: unresolved; no resolver link, observed 2026-08-11T12:18:42.764089Z

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

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types cites this paper.

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

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Observation 4d5ac949-2749-4882-a23e-89aece2ed911 · inbound

TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data cites this paper.

TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T00:18:41.459337Z

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Observation abd742b1-b456-4aa9-a041-1549768b5525 · inbound

LEAP: LLM-powered End-to-end Automatic Library for Processing Social Science Queries on Unstructured Data cites this paper.

LEAP: LLM-powered End-to-end Automatic Library for Processing Social Science Queries on Unstructured Data Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 125

Resolution: unresolved; no resolver link, observed 2026-08-10T21:51:04.334740Z

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Observation 8db7c4e9-ea1e-4336-94e0-7ecb703eb50f · inbound

Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility cites this paper.

Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T19:04:26.005585Z

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Observation 6fad8fb4-bb06-49ea-83be-ebc9108c684e · inbound

Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL cites this paper.

Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T17:17:24.473106Z

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Observation 850014d3-6495-46bb-bb58-22126fdb511a · inbound

Evaluating and Improving Graph to Text Generation with Large Language Models cites this paper.

Evaluating and Improving Graph to Text Generation with Large Language Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T15:11:01.323123Z

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Observation a68c9f11-79b6-4cb5-a7df-8323fb0031c0 · inbound

Rethinking Table Instruction Tuning cites this paper.

Rethinking Table Instruction Tuning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T14:59:58.629136Z

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Observation 21f8dfc9-6b20-4741-a53e-d302ac49f3f1 · inbound

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects cites this paper.

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T14:56:30.973161Z

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Observation a650569b-571c-4879-b3a8-f518c38e2c51 · inbound

Extractive Schema Linking for Text-to-SQL cites this paper.

Extractive Schema Linking for Text-to-SQL Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T15:28:34.497427Z

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Observation dd34f70d-5c6d-4220-9177-89ee85fd8894 · inbound

Querying Databases with Function Calling cites this paper.

Querying Databases with Function Calling Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T15:25:14.528460Z

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Observation 334aef28-f2e3-4be0-b144-280af8aee97c · inbound

M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference cites this paper.

M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T13:40:56.612810Z

Source-reported events for the cited work

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Observation 46e205f2-b69e-4815-b837-2371caca87a8 · inbound

Balancing Content Size in RAG-Text2SQL System cites this paper.

Balancing Content Size in RAG-Text2SQL System Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T11:12:28.906238Z

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Observation 7696ef25-2ee5-45ab-96d4-279a4b45cae2 · inbound

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding cites this paper.

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-05-22T19:52:01.839588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd27be79-36d5-47ea-8270-2e9b8411546e · inbound

Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index cites this paper.

Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T05:14:00.955108Z

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Observation 8dd2fb51-d834-4ee5-b1ff-e5752d85842e · inbound

Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning cites this paper.

Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T10:54:52.683948Z

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Observation 83cfd022-246e-4869-a456-bf113eecc912 · inbound

Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5 cites this paper.

Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5 Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:03:02.910694Z

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Observation bbb470d0-071a-476c-8b8f-3ca5ca5439a8 · inbound

Harmonia: End-to-End RAG Serving Optimization cites this paper.

Harmonia: End-to-End RAG Serving Optimization Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T04:40:04.787699Z

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Observation d30ad096-ba92-4151-92b7-f53511436fac · inbound

Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection cites this paper.

Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:36:34.210188Z

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Observation e3078a68-1bd5-4e6a-a81a-e2034b7a5fab · inbound

AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models cites this paper.

AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:30:40.826486Z

Source-reported events for the cited work

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Observation b98d5339-91c0-4828-b982-3c0b2f3fc4f0 · inbound

Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering cites this paper.

Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:25.833973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65d5757f-1e2f-4896-bcc4-8f11b3a42431 · inbound

Capturing the Effects of Quantization on Trojans in Code LLMs cites this paper.

Capturing the Effects of Quantization on Trojans in Code LLMs Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:32.400534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60112040-b5d2-4afe-9b92-9f30e9ee323d · inbound

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects cites this paper.

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:55:57.916408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf63939f-b89b-49ea-ba3b-c9473fbaae49 · inbound

UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification cites this paper.

UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:38:36.437972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 047e8bb3-d182-4bae-978d-8ef24c58cfdc · inbound

Meta-aware Learning in text-to-SQL Large Language Model cites this paper.

Meta-aware Learning in text-to-SQL Large Language Model Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:58.231953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28b1ee15-cfc7-4ca6-babc-97b15c62a238 · inbound

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

Automatic Metadata Extraction for Text-to-SQL Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 1996

Resolution: unresolved; no resolver link, observed 2026-08-07T14:07:09.851461Z

Source-reported events for the cited work

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Observation 1f7e146c-038d-4741-8944-43da98534dda · inbound

RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models cites this paper.

RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T13:32:50.822133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29c7764d-9742-4a6f-bc23-5160b8cab383 · inbound

StreamLink: Large-Language-Model Driven Distributed Data Engineering System cites this paper.

StreamLink: Large-Language-Model Driven Distributed Data Engineering System Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:56.271142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12fa1b7a-dbfd-42a7-9422-43887eb72028 · inbound

MMTABREAL: Real-World Benchmark for Multimodal Table Understanding cites this paper.

MMTABREAL: Real-World Benchmark for Multimodal Table Understanding Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.612472Z

Source-reported events for the cited work

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Observation 461cfaa1-1d4e-4a23-b095-9325224177a8 · inbound

TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation cites this paper.

TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T13:37:19.292463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a7de26d8-e3c9-4caa-bc19-ab169b858011 · inbound

MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps cites this paper.

MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:08.456955Z

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Observation f4fd0f2d-302f-4567-8054-18a397a81b32 · inbound

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning cites this paper.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:55.747677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d168ba4e-b278-4a52-a85c-360c8f939934 · inbound

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

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 166

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:48.116381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b04b40b4-60bd-4dd9-8541-75e9e2ada985 · inbound

LLM Inference Enhanced by External Knowledge: A Survey cites this paper.

LLM Inference Enhanced by External Knowledge: A Survey Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:23.047441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f838a6b-4c60-4c9b-90b8-d95bb9dde953 · inbound

SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning cites this paper.

SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:34.789925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation cites this paper.

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation 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 bf60c0e3-1421-44d6-bc8c-eab38c69dd14 · inbound

Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA cites this paper.

Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:00.275534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa35238b-96bf-4540-808e-a8ff383495cd · inbound

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions cites this paper.

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 232

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:31.691224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc484ef8-fe63-4bac-a6ec-04041b25f316 · inbound

MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space cites this paper.

MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:03.991559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83222422-b774-46d0-8883-00ea06a8be6e · inbound

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers cites this paper.

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T19:53:14.854294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64dd93f2-8b86-450c-a236-5cb4555ca603 · inbound

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing cites this paper.

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:02.264452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cd51436-3920-4350-92cc-fc3f2eb9c0e9 · inbound

STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing cites this paper.

STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:10:59.647092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a061464-ef55-4cea-8158-caa7e362e69a · inbound

DABstep: Data Agent Benchmark for Multi-step Reasoning cites this paper.

DABstep: Data Agent Benchmark for Multi-step Reasoning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:37:54.118777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7f622a-2c6a-4492-a4e7-342e9466f1c4 · inbound

M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis cites this paper.

M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-22T00:05:47.457876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44691bb6-fa21-4e1b-9247-58a7ec08dc1d · inbound

Interactive Text-to-SQL via Expected Information Gain for Disambiguation cites this paper.

Interactive Text-to-SQL via Expected Information Gain for Disambiguation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T19:17:17.090375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 766ae8fd-143a-4f23-843e-ae9b71687479 · inbound

THOR: Transformer Heuristics for On-Demand Retrieval cites this paper.

THOR: Transformer Heuristics for On-Demand Retrieval Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:02.659974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc799757-1f11-4dc2-b227-60f94657206b · inbound

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering cites this paper.

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:17.695236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68ab8640-84b7-4b39-bcc7-298303e3e711 · inbound

MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps cites this paper.

MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:36:52.389207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433472d6-3883-4b3c-a8df-c00da4741bea · inbound

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs cites this paper.

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T14:59:00.536075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae102c8-8f4a-4ccc-8899-3a2b7517f2d8 · inbound

Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text cites this paper.

Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: malformed identifier; no resolver link, observed 2026-08-06T13:58:02.358475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ecc00e4-0e01-42b1-b058-5292770e8697 · inbound

The Blessing and Curse of Dimensionality in Safety Alignment cites this paper.

The Blessing and Curse of Dimensionality in Safety Alignment Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T17:52:49.808075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7578b51-1415-4c46-baab-e65eb3fb0636 · inbound

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges cites this paper.

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:37.496053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a097b2d7-7950-49cb-8fa6-ba6ab29c868e · inbound

HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling cites this paper.

HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-18T21:41:51.690074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 789e3d54-d557-4e97-b2a7-eb5325009de5 · inbound

X-SQL: Expert Schema Linking and Understanding of Text-to-SQL with Multi-LLMs cites this paper.

X-SQL: Expert Schema Linking and Understanding of Text-to-SQL with Multi-LLMs Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:42.022484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ae32ed-2e65-414f-8bbb-07b242acc41b · inbound

PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning cites this paper.

PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T22:48:31.257641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4af8fc4-8c6c-4af4-91db-499fc3d40b47 · inbound

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images cites this paper.

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:42:47.576609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae80f4fc-e4cb-49e9-b567-407d3048e574 · inbound

Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation cites this paper.

Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T19:30:05.404465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd483d93-a997-47e1-8a78-add51da1a64a · inbound

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation cites this paper.

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T09:21:10.728539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1f76f2a-7887-4670-838d-5e03c1678f0b · inbound

CORE-T: COherent REtrieval of Tables for Text-to-SQL cites this paper.

CORE-T: COherent REtrieval of Tables for Text-to-SQL Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T09:41:42.550308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d3c3f5-6ed2-4217-a452-51b76108befd · inbound

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models cites this paper.

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T20:33:53.772586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb9f1de1-7a5d-4088-a13a-0109e5a999e3 · inbound

Large Language Model-Enhanced Relational Operators: Taxonomy, Benchmark, and Analysis cites this paper.

Large Language Model-Enhanced Relational Operators: Taxonomy, Benchmark, and Analysis Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 60

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T17:40:11.769402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1202f5a5-b513-4441-a755-f4b735421548 · inbound

An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data cites this paper.

An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T18:39:32.867967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c04a9b2-2ac5-432c-9180-164c6d3658e2 · inbound

Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions cites this paper.

Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-15T00:19:35.444309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11a5d5fd-0639-4dcb-a79c-1d460757b10a · inbound

From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing cites this paper.

From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-13T20:18:12.980907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93b7179b-7531-4501-a940-21e3cbf5e9d1 · inbound

From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing cites this paper.

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T13:49:52.461419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1c6b313-ccd2-4d56-b0ea-0383f186228f · inbound

AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views cites this paper.

AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebd8c64f-af45-4e91-a74d-0330983571a0 · inbound

ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification cites this paper.

ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6cbe1d3e-120b-4752-adfb-445c5cabc2c5 · inbound

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use cites this paper.

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:58:50.330371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.