Paper Citation Record · LEDGER

Evaluating LLMs for Visualization Generation and Understanding

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.22890.

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

pith.paper-citation-record.v1
2507.22890 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:39:46.231405Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1fa2293-471e-4e5d-aca9-87d8c50e6437 · outbound

This paper cites https://www.tableau.com/.

Evaluating LLMs for Visualization Generation and Understanding https://www.tableau.com/

Reference 1

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Observation c76888ba-db5d-4eff-8146-32cff6942716 · outbound

This paper cites https://matplotlib.org/.

Evaluating LLMs for Visualization Generation and Understanding https://matplotlib.org/

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:51.797859Z

Source-reported events for the cited work

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Observation 9a26e7d5-fe44-4487-aa07-47ce645264d4 · outbound

This paper cites https://vega.github.io/vega-lite/.

Evaluating LLMs for Visualization Generation and Understanding https://vega.github.io/vega-lite/

Reference 3

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Observation 7503643d-147b-4d58-b7d5-237e4ecb1de2 · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics 28(12), 5049–5070 (2022).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization and Computer Graphics 28(12), 5049–5070 (2022)

Reference 4

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Observation 314b9b67-88f8-4290-ad7c-3e5f6d6049c6 · outbound

This paper cites : Language mod- els are few-shot learners.

Evaluating LLMs for Visualization Generation and Understanding : Language mod- els are few-shot learners

Reference 5

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Observation 79cdddeb-d579-4dc4-a845-cdf355db9b07 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Evaluating LLMs for Visualization Generation and Understanding Evaluating Large Language Models Trained on Code

Reference 6

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Source-reported events for the cited work

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Observation 62dc78ab-a1bf-47c1-badf-fa196f5b8f48 · outbound

This paper cites https://https://openai.com/index/ gpt-4.

Evaluating LLMs for Visualization Generation and Understanding https://https://openai.com/index/ gpt-4

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:51.259546Z

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Observation 3b9d2ee6-d626-4256-a49a-140e598d9e50 · outbound

This paper cites https://gemini.google.com.

Evaluating LLMs for Visualization Generation and Understanding https://gemini.google.com

Reference 8

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Observation db6fea16-5523-4260-9446-a43919c15858 · outbound

This paper cites https://claude.ai.

Evaluating LLMs for Visualization Generation and Understanding https://claude.ai

Reference 9

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Observation c130941e-f454-4c9f-8f9f-776da686ff3c · outbound

This paper cites ACM Computing Surveys 55(9), 1–35 (2023).

Evaluating LLMs for Visualization Generation and Understanding ACM Computing Surveys 55(9), 1–35 (2023)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:50.731968Z

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Observation 607dd990-f717-4468-9173-3955713eecdd · outbound

This paper cites IEEE Transactions on Visualization & Computer Graphics 26(1), 895–905 (2020).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization & Computer Graphics 26(1), 895–905 (2020)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:50.559717Z

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Observation e7a8810f-290a-4462-bcfd-2874982a7c6e · outbound

This paper cites IEEE Computer Graphics and Applications 39(5), 33–46 (2019).

Evaluating LLMs for Visualization Generation and Understanding IEEE Computer Graphics and Applications 39(5), 33–46 (2019)

Reference 12

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Observation 128c9be0-fc08-4a79-bc91-cacd2fb12116 · outbound

This paper cites IEEE Transactions on Visual- ization & Computer Graphics 30(1), 284–294 (2024).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visual- ization & Computer Graphics 30(1), 284–294 (2024)

Reference 13

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Observation 04bc2ac6-456a-4b34-ae99-36bf5bd580d9 · outbound

This paper cites In: International ACM Conference on Management of Data (SIGMOD) (2018).

Evaluating LLMs for Visualization Generation and Understanding In: International ACM Conference on Management of Data (SIGMOD) (2018)

Reference 14

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Observation 5181588b-630b-43fc-a265-1d1aa98b674d · outbound

This paper cites http://opennlp.apache.

Evaluating LLMs for Visualization Generation and Understanding http://opennlp.apache

Reference 15

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Observation f4feb23a-a57a-402f-99aa-0b583bce55ec · outbound

This paper cites IEEE Transac- tions on Visualization and Computer Graph- ics 27(2) (2021).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transac- tions on Visualization and Computer Graph- ics 27(2) (2021)

Reference 16

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Observation 672d6bc4-c8cf-4ed4-9ff2-f5c29c172b47 · outbound

This paper cites In: Proceedings of 52nd Annual Meeting of the Association for Com- putational Linguistics: System Demonstra- tions, pp.

Evaluating LLMs for Visualization Generation and Understanding In: Proceedings of 52nd Annual Meeting of the Association for Com- putational Linguistics: System Demonstra- tions, pp

Reference 17

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Observation 923efff1-0640-4412-add5-e4ec3c40b4f9 · outbound

This paper cites IEEE Transactions on 23 Visualization and Computer Graphics 29(6), 3121–3144 (2023).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on 23 Visualization and Computer Graphics 29(6), 3121–3144 (2023)

Reference 18

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Observation 19bf92ec-15bf-4cf0-ae0f-9569d5a614f4 · outbound

This paper cites In: IEEE Pacific Visualization Symposium (PacificVis) 2021 (2021).

Evaluating LLMs for Visualization Generation and Understanding In: IEEE Pacific Visualization Symposium (PacificVis) 2021 (2021)

Reference 19

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Observation 40fd9fea-0842-4722-bd38-4f509318729e · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics 28(1), 217–226 (2022).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization and Computer Graphics 28(1), 217–226 (2022)

Reference 20

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Observation e0f14330-91cb-493d-87bf-e56f732caa56 · outbound

This paper cites In: Proceedings of SIGMOD (2021).

Evaluating LLMs for Visualization Generation and Understanding In: Proceedings of SIGMOD (2021)

Reference 21

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Observation 6f64d674-c1db-4bac-a389-02314d8cd3a7 · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics 31(1) (2025).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization and Computer Graphics 31(1) (2025)

Reference 22

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Observation 9e47d711-3826-4731-ad98-8c6e8437b89d · outbound

This paper cites 343–352 (2024).

Evaluating LLMs for Visualization Generation and Understanding 343–352 (2024)

Reference 23

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Observation 5d04f826-630b-4059-ac16-7d3fbc10fe28 · outbound

This paper cites https://arxiv.

Evaluating LLMs for Visualization Generation and Understanding https://arxiv

Reference 24

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Observation fc4339d1-2317-4f25-b85c-ce8c232079a8 · outbound

This paper cites In: IEEE Vis 2024 (2024).

Evaluating LLMs for Visualization Generation and Understanding In: IEEE Vis 2024 (2024)

Reference 25

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Observation cd276e78-f8ea-47cf-80e2-2570d9577dfd · outbound

This paper cites IEEE Access 11 (2023).

Evaluating LLMs for Visualization Generation and Understanding IEEE Access 11 (2023)

Reference 26

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Observation fbba5840-806b-49d4-8024-b4ceda94cae9 · outbound

This paper cites In: Proceedings of the 61st Annual Meet- ing of the Association for Computational Lin- guistics (Volume 3: System Demonstrations), pp.

Evaluating LLMs for Visualization Generation and Understanding In: Proceedings of the 61st Annual Meet- ing of the Association for Computational Lin- guistics (Volume 3: System Demonstrations), pp

Reference 27

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Observation 6e6ff2de-3fff-4006-8470-2574746dae82 · outbound

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

Evaluating LLMs for Visualization Generation and Understanding ChartLlama: A Multimodal LLM for Chart Understanding and Generation

Reference 28

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This paper cites In: Findings of the Association for Computational Lin- guistics: EMNLP 2023 (2023).

Evaluating LLMs for Visualization Generation and Understanding In: Findings of the Association for Computational Lin- guistics: EMNLP 2023 (2023)

Reference 29

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This paper cites In: Findings of the Asso- ciation for Computational Linguistics: ACL 2024 (2024).

Evaluating LLMs for Visualization Generation and Understanding In: Findings of the Asso- ciation for Computational Linguistics: ACL 2024 (2024)

Reference 30

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Evaluating LLMs for Visualization Generation and Understanding In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

Reference 31

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Observation 2b28aa00-13c0-46eb-adbe-ff800ce6f20e · outbound

This paper cites In: Findings of the Asso- ciation for Computational Linguistics: ACL, Dublin, Ireland (2022).

Evaluating LLMs for Visualization Generation and Understanding In: Findings of the Asso- ciation for Computational Linguistics: ACL, Dublin, Ireland (2022)

Reference 32

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This paper cites SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs.

Evaluating LLMs for Visualization Generation and Understanding SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs

Reference 33

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Observation 86a8135d-8b98-457a-b966-e7fb1ea72d01 · outbound

This paper cites In: Computer Vision and Pattern 24 Recognition (CVPR) (2024).

Evaluating LLMs for Visualization Generation and Understanding In: Computer Vision and Pattern 24 Recognition (CVPR) (2024)

Reference 34

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Observation b732d07f-6157-4c53-befa-bb34d23b15d8 · outbound

This paper cites ChartBench: A Benchmark for Complex Visual Reasoning in Charts.

Evaluating LLMs for Visualization Generation and Understanding ChartBench: A Benchmark for Complex Visual Reasoning in Charts

Reference 35

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This paper cites IEEE Transactions on Visualization and Computer Graphics 31(1), 1105–1115 (2025).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization and Computer Graphics 31(1), 1105–1115 (2025)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:46.651351Z

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Observation 35d67133-400c-424d-997a-aa7556ff531a · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics 31(1), 1116–1125 (2025).

Evaluating LLMs for Visualization Generation and Understanding IEEE Transactions on Visualization and Computer Graphics 31(1), 1116–1125 (2025)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:46.614919Z

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Observation 0e35f9a4-3180-42d4-bb84-3ac77565c5f9 · outbound

This paper cites In: IEEE Vis 2024 (2024).

Evaluating LLMs for Visualization Generation and Understanding In: IEEE Vis 2024 (2024)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:46.572349Z

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Observation 16450284-6e74-4ec3-8d8e-7e253851f9d0 · outbound

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Evaluating LLMs for Visualization Generation and Understanding FigureQA: An Annotated Figure Dataset for Visual Reasoning

Reference 39

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Observation 57cba96e-d381-48d9-9fd5-688d848299fd · outbound

This paper cites ACM Computing Surveys 55(12), 1–38 (2023).

Evaluating LLMs for Visualization Generation and Understanding ACM Computing Surveys 55(12), 1–38 (2023)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:39:46.529642Z

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Observation 8cfbada5-066f-42a4-a890-764d047d86f0 · outbound

This paper cites Neural Information Processing Systems (2022) 25.

Evaluating LLMs for Visualization Generation and Understanding Neural Information Processing Systems (2022) 25

Reference 41

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.