Paper Citation Record · LEDGER

Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2312.12464.

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

pith.paper-citation-record.v1
2312.12464 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:22:42.924594Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:34:45.722081Z

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 0676cd86-00a2-41c7-8f17-22010ca864cf · inbound

Improving LLM Group Fairness on Tabular Data via In-Context Learning cites this paper.

Improving LLM Group Fairness on Tabular Data via In-Context Learning Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T21:22:42.924594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 030607af-e0c7-49d8-8746-c13cec929aac · inbound

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data cites this paper.

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T11:32:53.941791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 190bb39d-b8e5-436f-9c8b-40196f5ed7fe · inbound

Knowledge prompt chaining for semantic modeling cites this paper.

Knowledge prompt chaining for semantic modeling Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:27:02.082954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4ad04aa-a2bc-408f-a54d-b826853e392d · inbound

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches cites this paper.

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:46.901690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b7fb643-96e7-42c6-9fbd-2bb716c1edcc · inbound

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting cites this paper.

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:00:22.431401Z

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 2c61745b-ff37-4e4e-b0df-e8b3ecd7461d · inbound

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems cites this paper.

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:08.718222Z

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 e966151c-31b3-4f46-b168-b631c2d7f1ee · inbound

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems cites this paper.

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:57.475466Z

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 919f64fc-1c85-4bd6-aa5e-11d25c2f17b1 · 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 Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:34:45.723675Z

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.