Paper Citation Record · LEDGER

Evaluating Large Language Models as Expert Annotators

As of 20 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2508.07827.

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

pith.paper-citation-record.v1
2508.07827 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:58:03.190450Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T21:36:22.389047Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T21:43:59.674035Z

Reference resolution

52 of 52 outbound references displayed

verified exact0
verified fuzzy18
unresolved34
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b936667-020d-4e5e-a448-86eb1033055c · outbound

This paper cites write newline.

Evaluating Large Language Models as Expert Annotators write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:58.492704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4c63d06-2427-4046-be84-810b6c61a8cf · outbound

This paper cites GPT-4 Technical Report.

Evaluating Large Language Models as Expert Annotators GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:58.634448Z

Source-reported events for the cited work

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Observation 1d58986c-7e14-4e38-8c82-ee71a1672364 · outbound

This paper cites Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning.

Evaluating Large Language Models as Expert Annotators Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:58.829185Z

Source-reported events for the cited work

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Observation b0c71ad9-7577-4825-b493-a4d9c14aa089 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

Evaluating Large Language Models as Expert Annotators The claude 3 model family: Opus, sonnet, haiku

Reference 4

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Source-reported events for the cited work

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Observation 529cf28d-af15-4939-80ac-bf7d8d69c3d7 · outbound

This paper cites Claude 3.7 sonnet and claude code.

Evaluating Large Language Models as Expert Annotators Claude 3.7 sonnet and claude code

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.436139Z

Source-reported events for the cited work

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Observation fb27085f-bb3b-4086-a198-57b7f2157896 · outbound

This paper cites Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost.

Evaluating Large Language Models as Expert Annotators Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:59.306767Z

Source-reported events for the cited work

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Observation f06ed9c3-bca1-4c83-b6eb-db6b4708b7c8 · outbound

This paper cites Must read: A systematic survey of computational persuasion.

Evaluating Large Language Models as Expert Annotators Must read: A systematic survey of computational persuasion

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:59.405114Z

Source-reported events for the cited work

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Observation bae1414c-9156-4b55-b678-7dd155b038f4 · outbound

This paper cites Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams.

Evaluating Large Language Models as Expert Annotators Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T21:57:59.514351Z

Source-reported events for the cited work

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Observation 1a55427f-961a-4075-a876-0c55903314d1 · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

Evaluating Large Language Models as Expert Annotators ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 9

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Source-reported events for the cited work

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Observation 5ae76b19-00d9-4629-9bc3-94eaa1c256df · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Evaluating Large Language Models as Expert Annotators Evaluating Large Language Models Trained on Code

Reference 10

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Source-reported events for the cited work

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Observation 517ec177-3791-4aa5-8cf0-ef478a09746b · outbound

This paper cites an unresolved cited work.

Evaluating Large Language Models as Expert Annotators Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation b4fce70c-4c5d-4064-8b83-7f70e29dbcd3 · outbound

This paper cites Chatgpt goes to law school.

Evaluating Large Language Models as Expert Annotators Chatgpt goes to law school

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.417901Z

Source-reported events for the cited work

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Observation 6872d044-9693-4b34-a660-6621fcd6be4b · outbound

This paper cites GPTs Are Multilingual Annotators for Sequence Generation Tasks.

Evaluating Large Language Models as Expert Annotators GPTs Are Multilingual Annotators for Sequence Generation Tasks

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f02c00c8-f313-47e0-b984-c949688251ec · outbound

This paper cites Is gpt-3 a good data annotator? In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.\ 11173--11195, 2023.

Evaluating Large Language Models as Expert Annotators Is gpt-3 a good data annotator? In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.\ 11173--11195, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.408117Z

Source-reported events for the cited work

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Observation a7c5c73a-8d72-48e5-9517-8f6c6df3b268 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Evaluating Large Language Models as Expert Annotators Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 15

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Source-reported events for the cited work

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Observation 17132b9e-4e9c-4631-bf10-a58a9008b6d5 · outbound

This paper cites Measuring the persuasiveness of language models, 2024.

Evaluating Large Language Models as Expert Annotators Measuring the persuasiveness of language models, 2024

Reference 16

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Source-reported events for the cited work

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Observation de7121dc-276f-4728-ab52-9717e23e1123 · outbound

This paper cites Measuring nominal scale agreement among many raters.

Evaluating Large Language Models as Expert Annotators Measuring nominal scale agreement among many raters

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.392774Z

Source-reported events for the cited work

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Observation e9c6e715-aa56-4a68-95f0-99378478ba55 · outbound

This paper cites Chatgpt outperforms crowd workers for text-annotation tasks.

Evaluating Large Language Models as Expert Annotators Chatgpt outperforms crowd workers for text-annotation tasks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.383309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56a7c1a1-538c-4428-95bd-b23114d5e49b · outbound

This paper cites Introducing gemini 2.0: our new ai model for the agentic era.

Evaluating Large Language Models as Expert Annotators Introducing gemini 2.0: our new ai model for the agentic era

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.373126Z

Source-reported events for the cited work

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Observation 986d6739-07a7-47d7-b125-516761e6fd95 · outbound

This paper cites Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models.

Evaluating Large Language Models as Expert Annotators Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.363839Z

Source-reported events for the cited work

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Observation b75d865b-3672-4cb4-b829-b76d31677352 · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Evaluating Large Language Models as Expert Annotators Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 21

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Observation b214e203-5069-48d5-a24d-1bd72469d499 · outbound

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

Evaluating Large Language Models as Expert Annotators AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

Reference 22

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Observation de5df38a-bde8-4855-9660-b973c203d59e · outbound

This paper cites Measuring massive multitask language understanding.

Evaluating Large Language Models as Expert Annotators Measuring massive multitask language understanding

Reference 23

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Source-reported events for the cited work

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Observation 4c4a376f-c68b-4e41-8805-cc10a7c880d8 · outbound

This paper cites CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review.

Evaluating Large Language Models as Expert Annotators CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Reference 24

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Observation 4f533cea-5aac-4b70-9d42-58f50de4ed87 · outbound

This paper cites Coda-19: Using a non-expert crowd to annotate research aspects on 10,000+ abstracts in the covid-19 open research dataset.

Evaluating Large Language Models as Expert Annotators Coda-19: Using a non-expert crowd to annotate research aspects on 10,000+ abstracts in the covid-19 open research dataset

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.347992Z

Source-reported events for the cited work

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Observation fad2b1ea-507f-4d2e-a830-d3b9e44fa91f · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering.

Evaluating Large Language Models as Expert Annotators Pubmedqa: A dataset for biomedical research question answering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.234433Z

Source-reported events for the cited work

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Observation b92f1980-6828-457c-bc87-e8a9b0266c36 · outbound

This paper cites Gpt-4 passes the bar exam.

Evaluating Large Language Models as Expert Annotators Gpt-4 passes the bar exam

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:05.085182Z

Source-reported events for the cited work

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Observation f50825cc-d97b-4807-baac-a61adcee6c86 · outbound

This paper cites Refind: Relation extraction financial dataset.

Evaluating Large Language Models as Expert Annotators Refind: Relation extraction financial dataset

Reference 28

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Source-reported events for the cited work

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Observation 48a1eac5-5a42-4160-a50b-fd833586a3fb · outbound

This paper cites Large language models are zero-shot reasoners.

Evaluating Large Language Models as Expert Annotators Large language models are zero-shot reasoners

Reference 29

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Observation 75c933e7-86b6-4513-8c8e-6d0ebec54055 · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Evaluating Large Language Models as Expert Annotators Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 30

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Observation c6e66b29-847b-4193-bdea-3249e44aa8c9 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Evaluating Large Language Models as Expert Annotators Self-refine: Iterative refinement with self-feedback

Reference 31

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Observation ac125cd0-dd4a-4c59-9341-46d9049aed29 · outbound

This paper cites Note on the sampling error of the difference between correlated proportions or percentages.

Evaluating Large Language Models as Expert Annotators Note on the sampling error of the difference between correlated proportions or percentages

Reference 32

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Observation 015ce650-1ee1-475e-ab60-f723fe4f1b30 · outbound

This paper cites Hello gpt4-o.

Evaluating Large Language Models as Expert Annotators Hello gpt4-o

Reference 33

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Observation 6380658b-753d-4b7e-a4a2-483069c51e97 · outbound

This paper cites Openai o3-mini.

Evaluating Large Language Models as Expert Annotators Openai o3-mini

Reference 34

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Observation bd27a646-139c-455a-a90f-9e1b16a8886b · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Evaluating Large Language Models as Expert Annotators Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 35

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Observation 39af5a28-0b05-4d68-a5d0-524cab284565 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Evaluating Large Language Models as Expert Annotators GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 36

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Observation 5f4f6826-db1a-4fe3-bf6e-c0ffabb20674 · outbound

This paper cites Trillion dollar words: A new financial dataset, task & market analysis.

Evaluating Large Language Models as Expert Annotators Trillion dollar words: A new financial dataset, task & market analysis

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:04.560419Z

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Observation 8d3760b4-75fb-4393-b1ec-f8a4ad59da4e · outbound

This paper cites Large language models encode clinical knowledge.

Evaluating Large Language Models as Expert Annotators Large language models encode clinical knowledge

Reference 38

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Observation e6f28250-193d-4583-8c0e-52a1c449e99c · outbound

This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

Evaluating Large Language Models as Expert Annotators Towards Expert-Level Medical Question Answering with Large Language Models

Reference 39

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Observation b23d2c8a-924e-4b8e-b24b-ea4a1e70e351 · outbound

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Evaluating Large Language Models as Expert Annotators Large Language Models for Data Annotation and Synthesis: A Survey

Reference 40

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Observation ae8488ec-0c4b-4283-b3ae-f7adc6aeb2bd · outbound

This paper cites Are Expert-Level Language Models Expert-Level Annotators?.

Evaluating Large Language Models as Expert Annotators Are Expert-Level Language Models Expert-Level Annotators?

Reference 41

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Observation 6bf80663-a3d0-4615-ab02-2226a7e484fe · outbound

This paper cites Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization.

Evaluating Large Language Models as Expert Annotators Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization

Reference 42

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Observation 666ab889-8119-4572-9afa-ea7a8261fe26 · outbound

This paper cites Foundational autoraters: Taming large language models for better automatic evaluation.

Evaluating Large Language Models as Expert Annotators Foundational autoraters: Taming large language models for better automatic evaluation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:04.187402Z

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Observation dab46878-2ba9-4c4a-a4cc-256cdbdafad0 · outbound

This paper cites Cord-19: The covid-19 open research dataset.

Evaluating Large Language Models as Expert Annotators Cord-19: The covid-19 open research dataset

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:03.971639Z

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Observation 1d12a2d4-d2c7-4b0b-ba50-5b488092f484 · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models.

Evaluating Large Language Models as Expert Annotators Self-consistency improves chain of thought reasoning in language models

Reference 45

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Observation 70c0cfab-0624-4c8e-896e-26cd8cd03869 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Evaluating Large Language Models as Expert Annotators Chain-of-thought prompting elicits reasoning in large language models

Reference 46

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Source-reported events for the cited work

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Evaluating Large Language Models as Expert Annotators The rise and potential of large language model based agents: A survey

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:58:03.823825Z

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Observation f22bc1e0-d7fa-4bd8-9ba3-941f36e36b57 · outbound

This paper cites LLMaAA: Making Large Language Models as Active Annotators.

Evaluating Large Language Models as Expert Annotators LLMaAA: Making Large Language Models as Active Annotators

Reference 48

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This paper cites Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks.

Evaluating Large Language Models as Expert Annotators Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks

Reference 49

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Evaluating Large Language Models as Expert Annotators @esa (Ref

Reference 50

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Observation 97540c71-885f-451f-a6d0-8d9139f62244 · outbound

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Evaluating Large Language Models as Expert Annotators Unresolved cited work

Reference 51

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Observation 5e82cdd5-a4b0-4c2c-8998-871c749e6f46 · outbound

This paper cites By leveraging additional inference-time compute, we explore whether individual LLMs can serve as a direct alternative to expert data annotators.

Evaluating Large Language Models as Expert Annotators By leveraging additional inference-time compute, we explore whether individual LLMs can serve as a direct alternative to expert data annotators

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T21:58:03.190450Z

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Pith citing papers

Observation c8a8e6be-c643-4a74-8401-59d26806e229 · inbound

Agentic-imodels: Evolving agentic interpretability tools via autoresearch cites this paper.

Agentic-imodels: Evolving agentic interpretability tools via autoresearch Evaluating Large Language Models as Expert Annotators

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:36.282145Z

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Observation 7de7f1b9-5adf-4e10-b065-2fa6ed303ab6 · inbound

Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation cites this paper.

Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation Evaluating Large Language Models as Expert Annotators

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:43:59.676021Z

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