Paper Citation Record · LEDGER

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.19718.

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

pith.paper-citation-record.v1
2412.19718 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:59:58.466519Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact7
verified fuzzy6
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c220bc16-53cc-4671-ac52-dbf989096cde · outbound

This paper cites VQA: Visual Question Answering.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture VQA: Visual Question Answering

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.296814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 448a6cf0-a717-4726-9729-e6d96585ae3f · outbound

This paper cites SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.976220Z

Source-reported events for the cited work

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

Observation 4d39e6fe-e205-4536-9442-00bfd920e2f7 · outbound

This paper cites Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.309054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d93eae8d-0d5e-456a-a274-552016faf0f2 · outbound

This paper cites CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.933750Z

Source-reported events for the cited work

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

Observation 0921e10a-74d9-4cad-98fa-ede24d9b55ed · outbound

This paper cites Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.320915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f0d4b1b-7ba6-47aa-b772-93335f381b44 · outbound

This paper cites ChartLlama: A Multimodal LLM for Chart Understanding and Generation.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture ChartLlama: A Multimodal LLM for Chart Understanding and Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.326823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56776824-19e7-49c1-bd0f-63a644040e14 · outbound

This paper cites and McMillan, C., (2022) Semantic Similarity Metrics for Evaluating Source Code Summarization.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and McMillan, C., (2022) Semantic Similarity Metrics for Evaluating Source Code Summarization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.110878Z

Source-reported events for the cited work

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

Observation 1e08c659-d70d-425f-983d-d391efcd37c9 · outbound

This paper cites GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.873303Z

Source-reported events for the cited work

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

Observation ede94a6a-4256-47cb-ba7d-c751693dfcb8 · outbound

This paper cites and De Rijke, M., (2007) Machine learning for question answering from tabular data.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and De Rijke, M., (2007) Machine learning for question answering from tabular data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.089838Z

Source-reported events for the cited work

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

Observation 185fafd5-6133-427e-a1d6-e4f4d54c8679 · outbound

This paper cites CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.351076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e814c0b-61a9-49b5-8493-6f4e2a97a168 · outbound

This paper cites TSQA: Tabular Scenario Based Question Answering.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture TSQA: Tabular Scenario Based Question Answering

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.829807Z

Source-reported events for the cited work

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

Observation 3423201c-da51-4d7f-9ca6-dea604f7a234 · outbound

This paper cites nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.362951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 236f04be-4685-4bcb-9f9e-bc814fb649c4 · outbound

This paper cites ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.787044Z

Source-reported events for the cited work

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

Observation df1cbd7e-bd6f-4d00-af8f-5d73fa3c81e9 · outbound

This paper cites Chat2VIS: Fine-Tuning Data Visualisations using Multilingual Natural Language Text and Pre-Trained Large Language Models.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Chat2VIS: Fine-Tuning Data Visualisations using Multilingual Natural Language Text and Pre-Trained Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.372876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d10849-dbef-4a73-9a4b-e845e46592ea · outbound

This paper cites ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.378381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d437fa1-68fd-43d7-84ae-72ce30315dd9 · outbound

This paper cites and Stasko, J., (2021) NL4DV: A toolkit for generating analytic specifications for data visualization from natural language queries.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and Stasko, J., (2021) NL4DV: A toolkit for generating analytic specifications for data visualization from natural language queries

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.071610Z

Source-reported events for the cited work

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

Observation 68b05633-bde5-4c72-86df-9514e5150c41 · outbound

This paper cites TabIQA: Table Questions Answering on Business Document Images.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture TabIQA: Table Questions Answering on Business Document Images

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.389935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74e383b4-2d85-43dd-90a9-b87451cfbce7 · outbound

This paper cites Text2Chart: A Multi-Staged Chart Generator from Natural Language Text.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Text2Chart: A Multi-Staged Chart Generator from Natural Language Text

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T23:59:58.708077Z

Source-reported events for the cited work

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

Observation f7b38d98-b84c-4ac6-abea-3ac47f350a39 · outbound

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

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.401057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de242ce7-fb69-41a7-849c-05391274fdc1 · outbound

This paper cites and Shah, S., (2023b) DocGraphLM: Documental Graph Language Model for Information Extraction.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and Shah, S., (2023b) DocGraphLM: Documental Graph Language Model for Information Extraction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.052767Z

Source-reported events for the cited work

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

Observation cc58d2f5-36c1-4ccb-aef7-84247d760d4e · outbound

This paper cites and Qu, H., (2022) A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and Qu, H., (2022) A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.033403Z

Source-reported events for the cited work

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

Observation 1fb2a94e-516c-4639-8644-0c0ed7ec16bd · outbound

This paper cites Natural Language Models for Data Visualization Utilizing nvBench Dataset.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Natural Language Models for Data Visualization Utilizing nvBench Dataset

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.664642Z

Source-reported events for the cited work

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

Observation f9c6f662-f848-41f0-ab57-79dcb198d6eb · outbound

This paper cites DBCopilot: Natural Language Querying over Massive Databases via Schema Routing.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture DBCopilot: Natural Language Querying over Massive Databases via Schema Routing

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.422455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 316c38d1-a3de-468b-991d-20b4506c983a · outbound

This paper cites and Qu, H., (2022) AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture and Qu, H., (2022) AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:59:59.013992Z

Source-reported events for the cited work

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

Observation 716b83ed-2060-484f-a109-5b3b30d217fc · outbound

This paper cites DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:59:58.621759Z

Source-reported events for the cited work

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

Observation b181b434-deb1-4027-905b-9735dd2e7d05 · outbound

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

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.438966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5626bc9-11c1-4b23-b126-db9b77ebda9d · outbound

This paper cites GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.444422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11063a1c-9a64-4da1-9b61-34678826f9c5 · outbound

This paper cites ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.449721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7e62b7d-d560-4d58-a00c-bdcc05e5a5f6 · outbound

This paper cites Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.455968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19b2009d-51c8-4dfd-b642-b79980801cad · outbound

This paper cites Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.461362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df004027-d119-4085-b54f-6105fe08a7af · outbound

This paper cites TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance.

Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T23:59:58.466519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.