Paper Citation Record · LEDGER

Large Language Models for Imbalanced Classification: Diversity makes the difference

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2510.09783.

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

pith.paper-citation-record.v1
2510.09783 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:38:14.264744Z

measured 34 of 34 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:36:45.173113Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T06:31:00.923980Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy0
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1687af39-92ba-4d81-be6f-6bad57774e17 · outbound

This paper cites Generating synthetic data in finance: opportunities, challenges and pitfalls.

Large Language Models for Imbalanced Classification: Diversity makes the difference Generating synthetic data in finance: opportunities, challenges and pitfalls

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.317839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3278013c-6b44-4014-aab8-fdec99b722a9 · outbound

This paper cites Table-to-text: Describing table region with natural language.

Large Language Models for Imbalanced Classification: Diversity makes the difference Table-to-text: Describing table region with natural language

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.474775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a231af2e-9d84-4b23-8098-7c810370e34b · outbound

This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

Large Language Models for Imbalanced Classification: Diversity makes the difference SciBERT: A Pretrained Language Model for Scientific Text

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.550564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27e6860-16ae-4a96-a08c-ef4471688413 · outbound

This paper cites Deep neural networks and tabular data: A survey.IEEE Transactions on Neural Networks and Learning Systems, 2022.

Large Language Models for Imbalanced Classification: Diversity makes the difference Deep neural networks and tabular data: A survey.IEEE Transactions on Neural Networks and Learning Systems, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.611293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf6a4d9-7e5e-45f1-a2e0-43cde536ce4e · outbound

This paper cites Language models are realistic tabular data generators.

Large Language Models for Imbalanced Classification: Diversity makes the difference Language models are realistic tabular data generators

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.723289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 393c69d6-1390-432f-8b22-655d9a9121f9 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Large Language Models for Imbalanced Classification: Diversity makes the difference Learning imbalanced datasets with label-distribution-aware margin loss

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:10.867377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87ebe2c1-6fa2-4480-bc9b-03a0ece0190f · outbound

This paper cites SMOTE: synthetic minority over-sampling technique.Journal of Artifi- cial Intelligence Research, 2002.

Large Language Models for Imbalanced Classification: Diversity makes the difference SMOTE: synthetic minority over-sampling technique.Journal of Artifi- cial Intelligence Research, 2002

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.025247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b170f6-f825-40e8-bc62-1467fd32081f · outbound

This paper cites Xgboost: A scalable tree boosting system.

Large Language Models for Imbalanced Classification: Diversity makes the difference Xgboost: A scalable tree boosting system

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.053000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 525d8356-abad-4f24-a4c7-d9c617af9d89 · outbound

This paper cites TabFact : A Large- scale Dataset for Table-based Fact Verification.

Large Language Models for Imbalanced Classification: Diversity makes the difference TabFact : A Large- scale Dataset for Table-based Fact Verification

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.224936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31561a2f-8958-497d-9b24-f19f21a6d0f3 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Large Language Models for Imbalanced Classification: Diversity makes the difference Class-balanced loss based on effective number of samples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.373691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8056b0d-aa12-4818-87b7-594b8c668a00 · outbound

This paper cites Generative Adversarial Nets.

Large Language Models for Imbalanced Classification: Diversity makes the difference Generative Adversarial Nets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.486989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0f16e19-690b-4b85-924c-ea1196ffa371 · outbound

This paper cites Why do tree-based models still outperform deep learning on tabular data?.

Large Language Models for Imbalanced Classification: Diversity makes the difference Why do tree-based models still outperform deep learning on tabular data?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.555708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29af3126-acdb-4a96-a901-b245b3a970bd · outbound

This paper cites Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning.

Large Language Models for Imbalanced Classification: Diversity makes the difference Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.624364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51a07cad-dc0a-4f25-a45f-0825dd2c8508 · outbound

This paper cites ADASYN: Adaptive synthetic sampling approach for imbalanced learning.

Large Language Models for Imbalanced Classification: Diversity makes the difference ADASYN: Adaptive synthetic sampling approach for imbalanced learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.690992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97c6bba2-d397-4df5-9463-045994746446 · outbound

This paper cites Learning from imbalanced data.IEEE Transactions on knowledge and data engineering, 2009.

Large Language Models for Imbalanced Classification: Diversity makes the difference Learning from imbalanced data.IEEE Transactions on knowledge and data engineering, 2009

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:11.857716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef3e056b-5c3b-49a6-b45e-53c583fa0d42 · outbound

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

Large Language Models for Imbalanced Classification: Diversity makes the difference Tabllm: Few-shot classification of tabular data with large language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.064073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77e377ba-cc2b-47fa-abca-a78328b11286 · outbound

This paper cites TaPas: Weakly Supervised Table Parsing via Pre-training.

Large Language Models for Imbalanced Classification: Diversity makes the difference TaPas: Weakly Supervised Table Parsing via Pre-training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.180448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26120b37-0fab-42eb-ba43-73d72b59c00f · outbound

This paper cites Oct-GAN: Neural ODE-based conditional tabular GANs.

Large Language Models for Imbalanced Classification: Diversity makes the difference Oct-GAN: Neural ODE-based conditional tabular GANs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.309173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af13d8e3-cf85-4a3f-9614-171e99d5a64c · outbound

This paper cites An introduction to variational autoencoders.Foundations and Trends in Machine Learning, 2019.

Large Language Models for Imbalanced Classification: Diversity makes the difference An introduction to variational autoencoders.Foundations and Trends in Machine Learning, 2019

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.431241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8c9a898-c81b-404f-bfcd-df7fce3ba667 · outbound

This paper cites Reliable fidelity and diversity metrics for generative models.

Large Language Models for Imbalanced Classification: Diversity makes the difference Reliable fidelity and diversity metrics for generative models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.587572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78487a1a-7643-4389-8181-3a74f631ce7c · outbound

This paper cites Generating realistic tabular data with large language models.

Large Language Models for Imbalanced Classification: Diversity makes the difference Generating realistic tabular data with large language models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.716070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b6d1986-e901-44fc-98e8-0adbe09b53d2 · outbound

This paper cites Fairness Improvement for Black-box Classifiers with Gaus- sian Process.Information Sciences, 2021.

Large Language Models for Imbalanced Classification: Diversity makes the difference Fairness Improvement for Black-box Classifiers with Gaus- sian Process.Information Sciences, 2021

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:12.872197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 297732de-d314-4fe4-9657-0f9dec564e59 · outbound

This paper cites Data Synthesis Based on Generative Adversarial Networks.Proceedings of the VLDB Endowment, 2018.

Large Language Models for Imbalanced Classification: Diversity makes the difference Data Synthesis Based on Generative Adversarial Networks.Proceedings of the VLDB Endowment, 2018

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.004282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a096402-9da5-4f95-bc2e-9262fe4b1596 · outbound

This paper cites Synthcity: facilitating innovative use cases of synthetic data in different data modalities.

Large Language Models for Imbalanced Classification: Diversity makes the difference Synthcity: facilitating innovative use cases of synthetic data in different data modalities

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.139067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2abd5fd-5707-4472-82ab-081cc0c617f3 · outbound

This paper cites A novel SMOTE-based re- sampling technique trough noise detection and the boosting procedure.

Large Language Models for Imbalanced Classification: Diversity makes the difference A novel SMOTE-based re- sampling technique trough noise detection and the boosting procedure

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.278897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41fbf94c-1234-4fa9-b3e6-1fe92ac7cf15 · outbound

This paper cites Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes.

Large Language Models for Imbalanced Classification: Diversity makes the difference Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.343876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85bd77d6-1c9c-4828-862b-32451eb07209 · outbound

This paper cites Tabular data: Deep learning is not all you need.Information Fusion, 2022.

Large Language Models for Imbalanced Classification: Diversity makes the difference Tabular data: Deep learning is not all you need.Information Fusion, 2022

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.468471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0288f043-4999-4d39-ae57-accd52fe7993 · outbound

This paper cites Some inequalities satisfied by the quantities of information of Fisher and Shannon.Information and Control, 1959.

Large Language Models for Imbalanced Classification: Diversity makes the difference Some inequalities satisfied by the quantities of information of Fisher and Shannon.Information and Control, 1959

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.616231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b155a655-7308-46ea-9ad1-262b300e244d · outbound

This paper cites Table meets LLM: Can large language models understand structured ta- ble data? a benchmark and empirical study.

Large Language Models for Imbalanced Classification: Diversity makes the difference Table meets LLM: Can large language models understand structured ta- ble data? a benchmark and empirical study

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.729629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b61be02-cb01-4108-9788-3f21db2a9795 · outbound

This paper cites RPT: relational pre-trained trans- former is almost all you need towards democratizing data preparation.

Large Language Models for Imbalanced Classification: Diversity makes the difference RPT: relational pre-trained trans- former is almost all you need towards democratizing data preparation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:13.972927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b919253b-d97b-4def-8a74-8ac584a880c3 · outbound

This paper cites Modeling tabular data using Conditional GAN.

Large Language Models for Imbalanced Classification: Diversity makes the difference Modeling tabular data using Conditional GAN

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:14.044236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7f276ac-b902-4211-9f02-19c27665195c · outbound

This paper cites Language-interfaced tabular oversampling via progressive imputation and self-authentication.

Large Language Models for Imbalanced Classification: Diversity makes the difference Language-interfaced tabular oversampling via progressive imputation and self-authentication

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:14.164083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d55943cf-f128-4cfc-b62e-93b35b470e95 · outbound

This paper cites Generative table pre-training empowers models for tabular prediction.

Large Language Models for Imbalanced Classification: Diversity makes the difference Generative table pre-training empowers models for tabular prediction

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T10:38:14.264744Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation a46915b6-6188-49b7-805a-94d20a74b67a · inbound

Retrieval Augmented Classification for Confidential Documents cites this paper.

Retrieval Augmented Classification for Confidential Documents Large Language Models for Imbalanced Classification: Diversity makes the difference

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:04.959484Z

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.