Paper Citation Record · LEDGER

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.05237.

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

pith.paper-citation-record.v1
2505.05237 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:14:58.246436Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

39 of 39 outbound references displayed

verified exact0
verified fuzzy21
unresolved18
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f20cffef-2c5c-4ec2-8094-80d1cfc56baa · outbound

This paper cites Tabnet: Attentive interpretable tabular learning.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Tabnet: Attentive interpretable tabular learning

Reference 1

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Source-reported events for the cited work

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Observation aa3f3d7f-eefa-4678-be77-9e4fb8c59e35 · outbound

This paper cites Random forests.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Random forests

Reference 4

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Source-reported events for the cited work

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Observation 1a60d463-18b6-4976-a1ce-9d02eacc01f2 · outbound

This paper cites Revisiting deep learning models for tabular data.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Revisiting deep learning models for tabular data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.711128Z

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

This paper cites Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning.

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

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Observation ff699573-a953-4b99-9579-0f873033ffcc · outbound

This paper cites Tabllm: Few-shot classification of tab- ular data with large language models.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Tabllm: Few-shot classification of tab- ular data with large language models

Reference 14

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Observation 46138f3e-2050-486d-a52a-a49c2985df01 · outbound

This paper cites Tabpfn: A transformer that solves small tabular classification prob- lems in a second.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Tabpfn: A transformer that solves small tabular classification prob- lems in a second

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.684000Z

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Observation 9624dc60-8ba6-4368-9c42-a83b0c9b0c4c · outbound

This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 16

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Source-reported events for the cited work

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Observation 37efbe9b-b54a-4503-9a51-dfeff93514b6 · outbound

This paper cites Lightgbm: A highly efficient gradient boost- ing decision tree.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Lightgbm: A highly efficient gradient boost- ing decision tree

Reference 17

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Observation 6d394974-88fb-49d4-9658-2e90569ee4a7 · outbound

This paper cites Self- normalizing neural networks.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Self- normalizing neural networks

Reference 20

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Source-reported events for the cited work

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Observation 99fc0581-8a41-4fcc-8953-e82e6c0392e3 · outbound

This paper cites Logistic regression.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Logistic regression

Reference 21

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Source-reported events for the cited work

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Observation e529be0a-e46c-4667-b7ae-5e6d39a6be34 · outbound

This paper cites Classification and regression trees.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Classification and regression trees

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.597616Z

Source-reported events for the cited work

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Observation 624fedc8-276b-4586-82be-04c45700db60 · outbound

This paper cites Tadam: Task dependent adaptive metric for improved few-shot learning.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Tadam: Task dependent adaptive metric for improved few-shot learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.570945Z

Source-reported events for the cited work

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Observation 6396c207-6889-4ce2-861e-6549df321b5e · outbound

This paper cites Hallucinations in llms: Understand- ing and addressing challenges.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Hallucinations in llms: Understand- ing and addressing challenges

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.557974Z

Source-reported events for the cited work

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Observation 38e01d0d-438d-4b92-89d1-fdb49d479349 · outbound

This paper cites Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Reference 27

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Observation 70e82105-2d4f-4a66-9132-1819770aa5f3 · outbound

This paper cites Catboost: unbiased boosting with categor- ical features.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Catboost: unbiased boosting with categor- ical features

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.542570Z

Source-reported events for the cited work

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Observation a88cc577-887f-407b-8206-cd9b785e5f94 · outbound

This paper cites Machine learning in healthcare: A review.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Machine learning in healthcare: A review

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.528883Z

Source-reported events for the cited work

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Observation 69f6e846-3070-4eac-b72f-dedc5a6bf4c4 · outbound

This paper cites TABLET: Learning From Instructions For Tabular Data.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning TABLET: Learning From Instructions For Tabular Data

Reference 30

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Source-reported events for the cited work

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Observation 6f86153b-184f-4278-a882-e2b46dd03f69 · outbound

This paper cites Prototypical networks for few-shot learning.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Prototypical networks for few-shot learning

Reference 31

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Source-reported events for the cited work

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Observation 73aa4592-2299-46c6-9696-2410b6a6637b · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 32

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Source-reported events for the cited work

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Observation c857dd51-a4cc-4158-b1f3-1f69062c10fc · outbound

This paper cites Autoint: Automatic feature interaction learning via self-attentive neural networks.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Autoint: Automatic feature interaction learning via self-attentive neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.506918Z

Source-reported events for the cited work

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Observation 8a7f0f1f-c055-4862-b70f-bbc7a7a6037b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning LLaMA: Open and Efficient Foundation Language Models

Reference 34

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Source-reported events for the cited work

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Observation cc342a38-6d2b-42b8-94bc-f9f99d173f3e · outbound

This paper cites Generalizing from a few ex- amples: A survey on few-shot learning.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Generalizing from a few ex- amples: A survey on few-shot learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.493048Z

Source-reported events for the cited work

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Observation 851c7126-3aa8-455e-9626-a615dd565c1f · outbound

This paper cites Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.480151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6283e018-d3ce-43d0-b786-ab5830a03159 · outbound

This paper cites Emergent Abilities of Large Language Models.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Emergent Abilities of Large Language Models

Reference 37

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Source-reported events for the cited work

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Observation 2652762c-7b52-4d70-a999-010334fcd8e6 · outbound

This paper cites From supervised to generative: A novel paradigm for tabular deep learning with large lan- guage models.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning From supervised to generative: A novel paradigm for tabular deep learning with large lan- guage models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.466481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40d4b7b8-8c42-4ed1-98bb-2dcd46ad7160 · outbound

This paper cites Vime: Extending the suc- cess of self-and semi-supervised learning to tabular do- main.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Vime: Extending the suc- cess of self-and semi-supervised learning to tabular do- main

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.451978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2cf4d2f6-e9d5-4033-89f9-35cef040d227 · outbound

This paper cites How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States

Reference 40

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Observation 17e6ede9-6eec-43b5-a78f-dc525b1f6f25 · outbound

This paper cites Ai in finance: challenges, techniques, and opportunities.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Ai in finance: challenges, techniques, and opportunities

Reference 2001

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Source-reported events for the cited work

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Observation 9399b5e7-bc52-4eb0-b4ca-6dea722a6ae3 · outbound

This paper cites D2r2: Diffusion-based representation with random distance matching for tabular few-shot learn- ing.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning D2r2: Diffusion-based representation with random distance matching for tabular few-shot learn- ing

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.609520Z

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Observation ae21a044-eac1-49ab-9ffd-7fb3642b08c2 · outbound

This paper cites Stunt: Few-shot tab- ular learning with self-generated tasks from unlabeled ta- bles.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Stunt: Few-shot tab- ular learning with self-generated tasks from unlabeled ta- bles

Reference 2011

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Source-reported events for the cited work

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Observation 0eae364e-71c5-4ace-960d-d2685d975406 · outbound

This paper cites A Closer Look at Few-shot Classification.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning A Closer Look at Few-shot Classification

Reference 2016

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Observation ee27c628-6502-4a64-834f-1a3b58fe33bc · outbound

This paper cites Tabnn: A universal neural network solution for tabular data.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Tabnn: A universal neural network solution for tabular data

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.655994Z

Source-reported events for the cited work

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Observation f9ff1756-ad48-4b51-bb42-1b4484739733 · outbound

This paper cites Deepgbm: A deep learning frame- work distilled by gbdt for online prediction tasks.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Deepgbm: A deep learning frame- work distilled by gbdt for online prediction tasks

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.643093Z

Source-reported events for the cited work

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Observation e1b36495-1896-4796-9228-ddb94c96501f · outbound

This paper cites ReConTab: Regularized Contrastive Representation Learning for Tabular Data.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning ReConTab: Regularized Contrastive Representation Learning for Tabular Data

Reference 2019

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This paper cites SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

Reference 2020

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Source-reported events for the cited work

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Observation 73be7746-9eec-4415-a00c-8e49baaf74f0 · outbound

This paper cites Gradient Boosting Neural Networks: GrowNet.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Gradient Boosting Neural Networks: GrowNet

Reference 2021

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Source-reported events for the cited work

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Observation 29dafd88-2a81-4635-9694-cd97848d37c9 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Xgboost: A scalable tree boosting system

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:14:58.722899Z

Source-reported events for the cited work

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Observation be13dde4-2e35-4b97-840c-34e3aba05ec0 · outbound

This paper cites INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

Reference 2023

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Source-reported events for the cited work

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Observation 3d757fed-74d1-41e7-bb13-85311c2515cd · outbound

This paper cites Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States.

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T23:14:58.124990Z

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Pith citing papers

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