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Paper Citation Record · LEDGER

Large Language Models are Diverse Role-Players for Summarization Evaluation

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2303.15078.

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

pith.paper-citation-record.v1
2303.15078 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:32:03.398570Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e322404a-6914-45e3-8d92-573764254254 · inbound

ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate cites this paper.

ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:03:18.856228Z

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.

source=pdf_text observed=2026-05-13T13:03:18.765496Z digest=sha256:43048a9a4dff70a342bb53191cd9282715c1392330db2165c8d0dc2f056063b6

Observation b2a8bbfa-34b7-4ecc-ba1a-907dbe45672d · inbound

Instruction-Following Evaluation for Large Language Models cites this paper.

Instruction-Following Evaluation for Large Language Models Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:36:00.980443Z

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.

source=arxiv_source observed=2026-05-24T05:34:04.002648Z digest=sha256:4afb83a18a8f85267c737a2bf6808bc94120f34cc16a95276557e72afcf99270

Observation 16a6dd50-1577-4b16-8e84-d866482674ec · inbound

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques cites this paper.

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T02:16:17.916365Z

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.

source=pdf_text observed=2026-05-15T02:16:17.875268Z digest=sha256:8f8619609047496a9e997d504dd746621eb0c93be57fc619b609254740c51d1f

Observation 78ad570d-338a-4aef-8e62-c34cbd16e3d7 · inbound

Can Large Language Models Serve as Evaluators for Code Summarization? cites this paper.

Can Large Language Models Serve as Evaluators for Code Summarization? Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-12T04:32:03.398570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:32:03.398570Z digest=sha256:8652cf62c67756a64013c8d42984c4906fc18c156221fb00c81c1779a6cddc07

Observation f2d21087-399b-46dd-9579-f986ea6fe816 · inbound

How Managers Perceive AI-Assisted Conversational Training for Workplace Communication cites this paper.

How Managers Perceive AI-Assisted Conversational Training for Workplace Communication Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 111

Resolution
unresolved
no resolver link, observed 2026-08-07T15:36:48.211995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:36:48.211995Z digest=sha256:6de67029433630e3e7e1cab85a3f059cf4d0881c29fa1c67c9b5407f497f4609

Observation 1bc3a056-2ff9-4d9f-afb9-9f01b5a78a8b · inbound

Literature Review Of Multi-Agent Debate For Problem-Solving cites this paper.

Literature Review Of Multi-Agent Debate For Problem-Solving Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T12:47:37.178344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:47:37.178344Z digest=sha256:518f071af3ca5f0461bea0d495e1e177622fc4426f86e02d334766560fc75ba5

Observation cb27a574-3cee-4b88-9c87-33477ef56e88 · inbound

Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies cites this paper.

Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies Large Language Models are Diverse Role-Players for Summarization Evaluation

Reference 29

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T04:04:47.561856Z

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.

source=pdf_text observed=2026-05-10T04:01:21.400171Z digest=sha256:c17f640c2a530743b5d01e93d0c393799825d6f69009db48330962672942dfc7