Paper Citation Record · LEDGER

Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2404.09491.

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

pith.paper-citation-record.v1
2404.09491 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:14:58.138529Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:07:45.115362Z

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 0910dcc6-faaa-4010-8d8e-ed3c9f7a9cb8 · inbound

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers cites this paper.

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:37:15.396400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5170c886-e2de-47c9-bc3f-fc6767c8f4fc · inbound

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning cites this paper.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:14:58.138529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe5358cd-5ebd-478a-b171-4b05442fb67f · inbound

AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents cites this paper.

AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:57.966976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d43e82f-28fe-41c4-9d58-d610c1e213fe · inbound

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation cites this paper.

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:57.212195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bc699f8-7884-40c1-a29b-e987b739bbd4 · inbound

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification cites this paper.

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T16:43:58.967453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd00c0ec-7256-4a86-9613-245e7aa7dcd1 · inbound

FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data cites this paper.

FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:02:23.203505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 29137dae-ddb3-45a2-91fd-add00d32d6f2 · inbound

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction cites this paper.

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T02:42:44.919032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5cbc801-8ff0-402e-9e7c-5d881d55d74c · inbound

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution cites this paper.

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:57:26.454654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation de42302c-9636-44d8-8527-adf46f3800d3 · inbound

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning cites this paper.

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:07:45.117086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 10ca8044-0bd0-4b57-afc7-15be289eafba · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-11T11:47:14.742492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a12b1bf-aec3-40d8-a8d0-9d7c3ac28224 · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T08:36:03.028181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.