Paper Citation Record · LEDGER

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts

As of 12 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2411.14896.

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

pith.paper-citation-record.v1
2411.14896 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:47:47.454125Z

measured 33 of 33 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:36:37.808364Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:24:50.157172Z

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy23
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6c3eb48-ff39-455c-a044-63f7eab981cb · outbound

This paper cites Survey on deep learning with class imbalance,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Survey on deep learning with class imbalance,

Reference 1

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Source-reported events for the cited work

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Observation 4f3dabfb-8cd8-485b-9e97-3c7befb82822 · outbound

This paper cites Text data augmentation for deep learning,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Text data augmentation for deep learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:47:47.293443Z

Source-reported events for the cited work

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Observation 81594427-7ea6-4b34-9dbb-5655befff1f9 · outbound

This paper cites Generative pre-trained transformer (GPT) in research: A systematic review on data augmentation,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Generative pre-trained transformer (GPT) in research: A systematic review on data augmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.987058Z

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Observation 9d6d51fb-f509-4bcb-8eae-bbff29ed4df6 · outbound

This paper cites An empirical survey of data augmentation for limited data learning in NLP,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts An empirical survey of data augmentation for limited data learning in NLP,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.967061Z

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Observation 5fbc2c2b-5d84-4bf9-aa6d-41c8e0756f80 · outbound

This paper cites Green values in crowdfunding projects,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Green values in crowdfunding projects,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.949317Z

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 2290cc56-dc0c-4a29-8274-9ee4b597f97d · outbound

This paper cites Detecting Mentions of Green Practices in Social Media Based on Text Classification,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Detecting Mentions of Green Practices in Social Media Based on Text Classification,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.931969Z

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 80823b5d-084b-4661-962c-949fde4ca685 · outbound

This paper cites Green practices: Ways to investigation,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Green practices: Ways to investigation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.913384Z

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 507d047a-5268-451e-8b25-24fae71539f0 · outbound

This paper cites EDA: Easy data augmentation techniques for boost- ing performance on text classification tasks,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts EDA: Easy data augmentation techniques for boost- ing performance on text classification tasks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.895802Z

Source-reported events for the cited work

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Observation 499e2f3f-f7b1-440c-b238-c9a0c511da13 · outbound

This paper cites AEDA: An easier data augmentation technique for text classification,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts AEDA: An easier data augmentation technique for text classification,

Reference 9

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Source-reported events for the cited work

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Observation bd9ddf2f-d461-43a0-92ab-e65bfb343faf · outbound

This paper cites Improving Neural Machine Translation Models with Monolingual Data.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Improving Neural Machine Translation Models with Monolingual Data

Reference 10

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Source-reported events for the cited work

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Observation 0718af48-c192-432b-8850-5f83af4ede4e · outbound

This paper cites Data augmentation using pre- trained transformer models,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Data augmentation using pre- trained transformer models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.865896Z

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 12deb49b-ee32-4164-915a-7afc838c9067 · outbound

This paper cites Conditional BERT contextual augmentation,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Conditional BERT contextual augmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.848067Z

Source-reported events for the cited work

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Observation af99e906-fae8-4514-aa2b-cbd56277bb54 · outbound

This paper cites AugGPT: Leveraging ChatGPT for Text Data Augmentation.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts AugGPT: Leveraging ChatGPT for Text Data Augmentation

Reference 13

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Source-reported events for the cited work

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Observation ce4e5998-4372-4324-982f-37afe20a0f81 · outbound

This paper cites Is ChatGPT the ultimate Data Augment- ation Algorithm?.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Is ChatGPT the ultimate Data Augment- ation Algorithm?

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.831786Z

Source-reported events for the cited work

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Observation b52e1f37-0e03-4e14-9e67-741c2ecdc63f · outbound

This paper cites Improving Text Classification with Large Language Model-Based Data Augmentation,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Improving Text Classification with Large Language Model-Based Data Augmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.815323Z

Source-reported events for the cited work

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Observation cb4d82bf-3142-4f96-9ae3-3c8a49302465 · outbound

This paper cites GPT3Mix: Leveraging large-scale language models for text augmentation,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts GPT3Mix: Leveraging large-scale language models for text augmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.797570Z

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 61394940-6778-497c-b27a-516acbaf29ff · outbound

This paper cites Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification

Reference 17

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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 5c1d6ee8-1e0f-4539-b1c7-8027ea0c8bcf · outbound

This paper cites Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.781221Z

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 33b426f9-95e5-49c0-95c6-3a6b88b0e45c · outbound

This paper cites Improving multiclass classification of fake news using BERT-based models and ChatGPT- augmented data,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Improving multiclass classification of fake news using BERT-based models and ChatGPT- augmented data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.764669Z

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 c8297dd9-7819-4b07-b025-9e45423045a2 · outbound

This paper cites From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.746926Z

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 aff49a90-d9af-4adc-a63a-213776c9f45d · outbound

This paper cites The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.728659Z

Source-reported events for the cited work

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Observation f660957a-b41d-463d-b6f5-a8d589bda6ab · outbound

This paper cites GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.710424Z

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 2aad0711-d533-48af-b015-8613855bce4e · outbound

This paper cites The importance of green practices to reduce consumption,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts The importance of green practices to reduce consumption,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.693659Z

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 c3d330e1-6e7d-4870-9c60-46d848fce691 · outbound

This paper cites A family of pretrained transformer language models for Russian,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts A family of pretrained transformer language models for Russian,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.676547Z

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 39fead69-b27d-44be-a259-d0a100b7dda7 · outbound

This paper cites ELECTRA: Pre- training text encoders as discriminators rather than generators,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts ELECTRA: Pre- training text encoders as discriminators rather than generators,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.658675Z

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 1fad016f-ea76-492d-9fa0-dcf9fc567760 · outbound

This paper cites Adaptation of deep bidirectional multilin- gual transformers for Russian language,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Adaptation of deep bidirectional multilin- gual transformers for Russian language,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.629013Z

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 e7e2459e-f6e7-422d-861b-8d4412745898 · outbound

This paper cites BERT: Pre- training of deep bidirectional transformers for language understanding,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.609841Z

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 1e3eb4fa-84e9-4c01-88cc-9e7f01d1a2ae · outbound

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Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Simple transformers,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.591347Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1772070d-0d35-41e1-bb98-46ab02095f8b · outbound

This paper cites ROUGE: A package for automatic evaluation of summaries,.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts ROUGE: A package for automatic evaluation of summaries,

Reference 29

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Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts BERTScore: Evaluating text generation with BERT,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:47:47.563732Z

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 114f45e8-4996-4162-81c7-1d81340a429c · outbound

This paper cites Available: https://openreview.net/pdf?id=r1xMH1BtvB.

Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts Available: https://openreview.net/pdf?id=r1xMH1BtvB

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T14:47:47.429308Z

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Pith citing papers

Observation 76b97e46-20bc-4592-949b-596996b46fe9 · inbound

Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey cites this paper.

Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts

Reference 177

Resolution: unresolved; no resolver link, observed 2026-08-10T04:36:37.808364Z

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Observation c7109ed6-f414-4c10-86d8-f8ec727b32a9 · inbound

When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification cites this paper.

When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:24:50.158436Z

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.