Paper Citation Record · LEDGER

Feeding LLM Annotations to BERT Classifiers at Your Own Risk

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2504.15432.

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

pith.paper-citation-record.v1
2504.15432 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:30:20.156972Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

38 of 38 outbound references displayed

verified exact5
verified fuzzy2
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94f4ab94-78e0-4c66-8030-3361ad57704d · outbound

This paper cites write newline.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:19.975754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4f8db5d-c541-4a21-a0c2-6a1149e45f8f · outbound

This paper cites Comprehensive Exploration of Synthetic Data Generation: A Survey.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Comprehensive Exploration of Synthetic Data Generation: A Survey

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:19.982647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f82877a-28be-40ee-a905-823ef2862bc9 · outbound

This paper cites Pathologies of Pre-trained Language Models in Few-shot Fine-tuning.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:30:20.692653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b7130589-3a3d-4b8c-b4a7-ceed17ae5afb · outbound

This paper cites A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:19.993978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3cb1865-61e8-4011-bc0c-cb0957b5c079 · outbound

This paper cites SaulLM-7B: A pioneering Large Language Model for Law.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk SaulLM-7B: A pioneering Large Language Model for Law

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:19.998874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d14214d9-eff2-4ed4-bbc5-1c89142dd3ba · outbound

This paper cites LlamBERT: Large-scale low-cost data annotation in NLP.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk LlamBERT: Large-scale low-cost data annotation in NLP

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.003906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 718093ae-25a6-4bd6-a1b8-8e47499c9c9a · outbound

This paper cites Automated Hate Speech Detection and the Problem of Offensive Language.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Automated Hate Speech Detection and the Problem of Offensive Language

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.009012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcea62a3-7af3-4382-9c81-7ed6ab4b2e77 · outbound

This paper cites Deep Learning for Economists.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Deep Learning for Economists

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:30:20.610706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e847635-7508-427b-b66b-b379d3bee417 · outbound

This paper cites Calibration of pre-trained transformers.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Calibration of pre-trained transformers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.019774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebeac032-1f32-410c-ac7f-410b80a68869 · outbound

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

Feeding LLM Annotations to BERT Classifiers at Your Own Risk BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-16T11:30:20.024013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f61cae-ce41-469e-8018-cb6da6f74089 · outbound

This paper cites Text clustering applied to unbalanced data in legal contexts.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Text clustering applied to unbalanced data in legal contexts

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:30:20.778494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5820b242-7320-46b4-939d-13a30dd93092 · outbound

This paper cites Bias and Fairness in Large Language Models: A Survey.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Bias and Fairness in Large Language Models: A Survey

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.033778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 535e9b86-4b5a-4b5e-a843-1be839d45cc4 · outbound

This paper cites an unresolved cited work.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.039268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d1f8f5-5a6b-4e8a-9686-4ce4028a191c · outbound

This paper cites Fabricator: An open source toolkit for generating labeled training data with teacher llms.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Fabricator: An open source toolkit for generating labeled training data with teacher llms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:30:20.763593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11079a27-2118-4f50-9048-0ded66701b02 · outbound

This paper cites an unresolved cited work.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.048683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11807771-3829-452d-899c-dc124ef4387c · outbound

This paper cites Bias/variance decompositions for likelihood-based estimators.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Bias/variance decompositions for likelihood-based estimators

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.053078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 774f9378-5ef0-4347-9f7e-2b169f3f1bb1 · outbound

This paper cites Hopkins, Yphtach Lelkes, and Samuel Wolken.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Hopkins, Yphtach Lelkes, and Samuel Wolken

Reference 17

Resolution: verified exact; doi, observed 2026-08-16T11:30:20.290437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e796794-a5e0-413f-9640-d9cc34c5d3f5 · outbound

This paper cites A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.062959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd3f99c-8564-49d5-af6a-f890a0195312 · outbound

This paper cites MedSyn: LLM-Based Synthetic Medical Text Generation Framework, pp.\ 215–230.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk MedSyn: LLM-Based Synthetic Medical Text Generation Framework, pp.\ 215–230

Reference 19

Resolution: verified exact; doi, observed 2026-08-16T11:30:20.275409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b246b975-7649-4fe2-aace-0706a3d769a4 · outbound

This paper cites Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.072651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74a1f0c3-9f19-4aaf-b77d-b7ab2e079c82 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Gonzalez, Hao Zhang, and Ion Stoica

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.077480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cc1fe9d-6f3e-4ff1-839f-00975544f1d1 · outbound

This paper cites Synthetic data generation with large language models for text classification: Potential and limitations.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Synthetic data generation with large language models for text classification: Potential and limitations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.081952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab1f41ee-fd26-4675-be1d-f74c21e9f595 · outbound

This paper cites an unresolved cited work.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:30:20.739865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 07a6799f-74bd-49f1-ba25-1879151f0edc · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.090937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2907371d-a1aa-4030-80a8-bfcb11f72c15 · outbound

This paper cites Maas, Raymond E.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Maas, Raymond E

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.095623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43dab5eb-7c62-4656-acbe-3a9f4e55c581 · outbound

This paper cites Leveraging llms for fair data labeling and validation in crowdsourcing environments [vision paper].

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Leveraging llms for fair data labeling and validation in crowdsourcing environments [vision paper]

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.100654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dc5b601-997f-4111-9f5a-19a986d78c95 · outbound

This paper cites Replication Data for: The Temporal Focus of Campaign Communication , 2020.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Replication Data for: The Temporal Focus of Campaign Communication , 2020

Reference 27

Resolution: verified exact; doi, observed 2026-08-16T11:30:20.249135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64c72a12-a324-47a3-8370-ba303dc35211 · outbound

This paper cites Knowledge distillation in automated annotation: Supervised text classification with LLM -generated training labels.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Knowledge distillation in automated annotation: Supervised text classification with LLM -generated training labels

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.109895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d8156e5-1165-4a8c-91ed-84c8eb795fce · outbound

This paper cites Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.114614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d4ed707-b67c-48be-b5d8-2512fbda1acd · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.119536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89cd599c-a137-4e47-8afc-ae65aa1b259a · outbound

This paper cites Ai models collapse when trained on recursively generated data.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Ai models collapse when trained on recursively generated data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.124098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81e43df7-b86d-4a88-a57b-6805456ac35b · outbound

This paper cites Large language models and synthetic health data: progress and prospects.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Large language models and synthetic health data: progress and prospects

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.128602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aa34373-e6ef-4940-adb2-c04058c8f116 · outbound

This paper cites How to Fine-Tune BERT for Text Classification?.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk How to Fine-Tune BERT for Text Classification?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.133150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c455d30-819e-4598-8da7-f2b32858f782 · outbound

This paper cites Want to reduce labeling cost? GPT -3 can help.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Want to reduce labeling cost? GPT -3 can help

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.137655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d55a45ac-06d7-478c-af76-3193866cd259 · outbound

This paper cites Efficient Guided Generation for Large Language Models.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Efficient Guided Generation for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.142062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 740fe299-f15b-4f06-9839-de5f08670d40 · outbound

This paper cites Transformers: State-of-the-art natural language processing.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Transformers: State-of-the-art natural language processing

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.147276Z

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Unavailable: canonical work link unavailable.

Observation 48f69db1-95c5-4bee-b00a-1d8056d0447d · outbound

This paper cites Qwen2.5 Technical Report.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk Qwen2.5 Technical Report

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.151871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c72f1d1f-1e65-4c8b-bf0e-5cb03794ab9d · outbound

This paper cites On the calibration of large language models and alignment.

Feeding LLM Annotations to BERT Classifiers at Your Own Risk On the calibration of large language models and alignment

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:30:20.156972Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.