Paper Citation Record · LEDGER

Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2305.13062.

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

pith.paper-citation-record.v1
2305.13062 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:47:00.264591Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:34:45.717214Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a6881b69-46fb-44dc-aa32-f3888463bc83 · 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 Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:00.264591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1399d7f-bb78-4f69-acec-02d4b62a02d0 · inbound

AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research cites this paper.

AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T16:30:31.078561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9fa067d-baec-43cc-be8c-6d0d713e7e2f · inbound

Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs cites this paper.

Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:05.574016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2b0d76c-c643-4a41-83c6-747fa66505a8 · 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 Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:37.480621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 624086e3-0f87-4b75-b66a-8b659d7dd924 · inbound

Prompt Orchestration Markup Language cites this paper.

Prompt Orchestration Markup Language Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-05T18:53:55.472199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a0bccd-55d8-485c-8102-2e59811f2a18 · inbound

PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries cites this paper.

PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:32:54.754851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8c8ee8ca-d48c-4934-aa20-3fc26bdf4d93 · inbound

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots cites this paper.

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:34:45.718563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.