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Paper Citation Record · LEDGER

Are Expert-Level Language Models Expert-Level Annotators?

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.03254.

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

pith.paper-citation-record.v1
2410.03254 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:09:55.773665Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T00:02:17.562253Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b596c50b-a255-4471-8d87-4f96212a20ae · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods Are Expert-Level Language Models Expert-Level Annotators?

Reference 229

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:08:35.522811Z

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.

source=pdf_text observed=2026-05-11T23:08:34.312466Z digest=sha256:ae664e39deff3afdda3a127061c9c8bd5d8c33440843fa0d2f5014bfc613730c

Observation b32719bc-b44b-438a-b58d-6cdea0a1f50e · inbound

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators cites this paper.

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators Are Expert-Level Language Models Expert-Level Annotators?

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-23T00:02:17.565591Z

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.

source=pdf_text observed=2026-05-22T23:59:27.228553Z digest=sha256:f676eef8376e51a01caed637cb54c0de9d17450ea0547187f5517826a6dda87c

Observation dfb6a41e-ca9b-4232-8f12-83f7757c1b8e · inbound

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG cites this paper.

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG Are Expert-Level Language Models Expert-Level Annotators?

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:55.773665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:55.773665Z digest=sha256:cb60d4316da39f67544c5037bb81b20e6e400d016ee73f62fc7fafeeff0ffd65

Observation 0444d3b7-56b1-4080-a2be-6c082b071ce8 · inbound

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems cites this paper.

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems Are Expert-Level Language Models Expert-Level Annotators?

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T14:56:12.161058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:56:12.161058Z digest=sha256:fd7fcc2740f62edeb9d9f4db6f257f2cda094479bcfe617591b6c0908994eaf8

Observation 7332f556-fb7b-4d98-8c80-e1639ca2cc40 · inbound

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection cites this paper.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection Are Expert-Level Language Models Expert-Level Annotators?

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T21:53:06.230654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:53:06.230654Z digest=sha256:1df2af8ae4830e6fd06f272f14dc42d9f042301ec193ccbe1fb28877f52a5d9d

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

Evaluating Large Language Models as Expert Annotators cites this paper.

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

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T21:58:02.244277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:58:02.244277Z digest=sha256:b13f85ea2df92918760dcedd40f94ed976d3bee7e13001e3a043fe76c5abeb35