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Paper Citation Record · LEDGER

LLMs as Data Annotators: How Close Are We to Human Performance

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2504.15022.

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

pith.paper-citation-record.v1
2504.15022 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:37:54.009214Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

42 of 42 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved39
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f87e88db-34c0-4ad7-b0d5-15c75aff32d9 · outbound

This paper cites online" 'onlinestring :=.

LLMs as Data Annotators: How Close Are We to Human Performance online" 'onlinestring :=

Reference 1

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Observation a85a2bdf-ee86-45e9-9d1d-d0200a40c7bc · outbound

This paper cites write newline.

LLMs as Data Annotators: How Close Are We to Human Performance write newline

Reference 2

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source=arxiv_source observed=2026-08-16T11:37:53.875130Z digest=sha256:e32cf60d6d4b013fc24a24fa3abd57a4fcae092f229700e7ee4898cadf9d9916

Observation fd0f8ae7-ce90-43b9-9f77-d66fa6ffe40a · outbound

This paper cites NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data.

LLMs as Data Annotators: How Close Are We to Human Performance NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data

Reference 3

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Observation 98d752fa-2287-4ffc-bca9-4879138a220a · outbound

This paper cites Language Models are Few-Shot Learners.

LLMs as Data Annotators: How Close Are We to Human Performance Language Models are Few-Shot Learners

Reference 4

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Observation 2c404c9a-2993-43b1-93ee-022d33f6aa31 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 5

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a6069c81-aed1-4d62-b37f-5556c57a67ae · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 6

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Observation 8de51acb-e75b-4704-932f-888657bc96c0 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

LLMs as Data Annotators: How Close Are We to Human Performance BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Observation 7713846a-3a7f-475c-b35d-29c4a87e37c6 · outbound

This paper cites A Survey on In-context Learning.

LLMs as Data Annotators: How Close Are We to Human Performance A Survey on In-context Learning

Reference 8

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Observation a08a408c-8489-403a-9d93-aa9bdbc3f81a · outbound

This paper cites The Faiss library.

LLMs as Data Annotators: How Close Are We to Human Performance The Faiss library

Reference 9

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Observation 54462c22-e831-4e07-86ee-1e0aeaed08e8 · outbound

This paper cites What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering.

LLMs as Data Annotators: How Close Are We to Human Performance What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering

Reference 10

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Observation e2ed7a3d-5378-4392-8cff-360e91485b7b · outbound

This paper cites Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy.

LLMs as Data Annotators: How Close Are We to Human Performance Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy

Reference 11

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Observation 696fa28d-65eb-4135-8822-3d3524ff932f · outbound

This paper cites LLMs Accelerate Annotation for Medical Information Extraction.

LLMs as Data Annotators: How Close Are We to Human Performance LLMs Accelerate Annotation for Medical Information Extraction

Reference 12

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Observation 572ce70a-eb50-4809-b4f5-33a290f91f75 · outbound

This paper cites AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators.

LLMs as Data Annotators: How Close Are We to Human Performance AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

Reference 13

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Observation 99e3e1ee-5a60-4100-88c4-082e18aecd7a · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 14

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Observation 77db4425-98fa-47d6-ba42-f62489c89693 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 15

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Observation 115aa345-d0c7-431e-80b8-b686ae514aa5 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 16

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Observation 258ed6b5-62c8-4e8f-b5f9-55ddc8a3f16f · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 17

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Observation 0217cb92-a2c0-4c48-ac54-ecd130a0fba5 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\.

LLMs as Data Annotators: How Close Are We to Human Performance u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\

Reference 18

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Observation dcf0a76a-9842-4a75-804e-ca0b8e2685af · outbound

This paper cites A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction.

LLMs as Data Annotators: How Close Are We to Human Performance A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction

Reference 19

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b46d9b48-2218-4451-b8a8-2919260d5f1c · outbound

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

LLMs as Data Annotators: How Close Are We to Human Performance RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 20

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Observation 58f7cc4f-15af-46e3-9d27-5844d73e9b16 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 21

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Observation aa1f94a2-4920-4398-a982-9e9a03b957fa · outbound

This paper cites Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation.

LLMs as Data Annotators: How Close Are We to Human Performance Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation

Reference 22

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Observation 0ec2329d-0c2e-4adc-8a17-5e0e55a6d6f1 · outbound

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LLMs as Data Annotators: How Close Are We to Human Performance GPT-4 Technical Report

Reference 23

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Observation edfe6673-c24d-495a-a02d-c428e03ab43c · outbound

This paper cites Pereira, Anabela Afonso, and Fátima Medeiros.

LLMs as Data Annotators: How Close Are We to Human Performance Pereira, Anabela Afonso, and Fátima Medeiros

Reference 24

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Observation d76752c1-de53-4ae5-908e-43cd8a1bd946 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

LLMs as Data Annotators: How Close Are We to Human Performance Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 25

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Observation 0b4f7aa0-4eda-4651-a77f-5cb0af0f1d03 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation bac0c30f-23b3-4a1d-9f49-7e10b2b6e729 · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

LLMs as Data Annotators: How Close Are We to Human Performance Large Language Models for Data Annotation and Synthesis: A Survey

Reference 27

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Observation 6524bdd1-d08c-44e9-bc5f-b6d8d927f62f · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

LLMs as Data Annotators: How Close Are We to Human Performance Gemini: A Family of Highly Capable Multimodal Models

Reference 28

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Observation eec58757-908f-4fab-924b-66433db454d9 · outbound

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LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 29

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Observation c7170c76-e1a4-402e-ac11-a2185f0403ee · outbound

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LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 30

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Observation b5b51df3-5f7d-4ba8-930a-76cc2058ba5f · outbound

This paper cites Tjong Kim Sang and Fien De Meulder.

LLMs as Data Annotators: How Close Are We to Human Performance Tjong Kim Sang and Fien De Meulder

Reference 31

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7572e7a3-bb75-48fa-80c7-a71c37476666 · outbound

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LLMs as Data Annotators: How Close Are We to Human Performance LLaMA: Open and Efficient Foundation Language Models

Reference 32

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LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 33

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Observation c30aecb0-86cc-497e-ae77-ab899eb22d0f · outbound

This paper cites LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey.

LLMs as Data Annotators: How Close Are We to Human Performance LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey

Reference 34

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Observation 5e9b31c5-b1f9-4c25-a002-897b303b40cb · outbound

This paper cites GPT-NER: Named Entity Recognition via Large Language Models.

LLMs as Data Annotators: How Close Are We to Human Performance GPT-NER: Named Entity Recognition via Large Language Models

Reference 35

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Observation f3e4aebb-5b61-4ca4-8f7b-6737e63e9b2a · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 36

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Observation 276fb61b-51f8-4525-86e4-eb9c6df7328a · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

LLMs as Data Annotators: How Close Are We to Human Performance Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 37

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Observation 18ddbafd-e9c3-47db-badd-172e4fb9ab7a · outbound

This paper cites LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples.

LLMs as Data Annotators: How Close Are We to Human Performance LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6ffd4933-f02f-4bbf-9819-ec95b2ff96ac · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T11:37:53.999374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:37:53.999374Z digest=sha256:3d385ddb523e79b0a268bfebcb6fb93e34421dd46ca63698e0c3dd3d95a6e990

Observation ff64fef8-fa23-403a-bd62-e96ae5f68681 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-16T11:37:54.500794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 503ded88-59a7-4f37-af47-87c67b39c6da · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

LLMs as Data Annotators: How Close Are We to Human Performance Automatic Chain of Thought Prompting in Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T11:37:54.006095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:37:54.006095Z digest=sha256:a303ec3ada611a92f91a8d72352174ee4337672b39e9f740b66c41ceddb2f954

Observation 945c7412-dd98-404a-ba44-f3bdbef445c9 · outbound

This paper cites an unresolved cited work.

LLMs as Data Annotators: How Close Are We to Human Performance Unresolved cited work

Reference 42

Resolution
verified exact
doi, observed 2026-08-16T11:37:54.040469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Pith citing papers

No inbound Pith citation observations are available.