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Paper Citation Record · LEDGER

Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.14122.

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

pith.paper-citation-record.v1
2310.14122 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:32:14.696386Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T23:40:11.173336Z

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 d594d381-8af9-4b67-8242-f26919e4d38a · inbound

RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze! cites this paper.

RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze! Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-15T23:40:11.176928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-15T23:40:11.018808Z digest=sha256:f8fc832167b973725ad9c5949c7a4ac81472963d67d4d4f333233b0ed7979033

Observation 37c019fa-5607-44a5-8a64-75c36371abde · inbound

SHIELD: APT Detection and Intelligent Explanation Using LLM cites this paper.

SHIELD: APT Detection and Intelligent Explanation Using LLM Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-09T12:32:14.696386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:32:14.696386Z digest=sha256:c59494d2588bbf8fd3daee2236efcbefe6d6b55ccc2735b2fe8070de8dc99b6b

Observation 216519be-27bd-405d-88fc-f110faf8a6b0 · inbound

Leveraging LLMs to Evaluate Usefulness of Document cites this paper.

Leveraging LLMs to Evaluate Usefulness of Document Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-07T05:11:38.449070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:11:38.449070Z digest=sha256:c05b62ecba760a9e4bf38823f2e07c9daab0b58caf4f8b1fcb402ba8cb762184

Observation 4e2e1e40-9944-4f93-bb34-b419052016b6 · inbound

MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval cites this paper.

MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T00:57:04.893838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:57:04.893838Z digest=sha256:c4c1c6a5fbcced5c01b2bcc922703b5bf15fa8f9d8cd1cf30ddd093f829e8b9c

Observation efeda6ba-be8b-47ba-8eaf-5694b9b77d4c · inbound

JointRank: Rank Large Set with Single Pass cites this paper.

JointRank: Rank Large Set with Single Pass Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T22:14:09.019331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:14:09.019331Z digest=sha256:d0e8d4d300879f2a1702e9eac9c288f9052c00819eaa2006277d47841a4aba24

Observation eb8c98d1-afdd-4260-898d-d95aa0930592 · inbound

Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation cites this paper.

Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T19:43:57.314346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:43:57.314346Z digest=sha256:786d460bf1daa3a24f561795ac05eb21e922f185886c044f436aba76e52b9305

Observation f7294ef5-c64b-44ff-9152-1ecf1040aba2 · inbound

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-05T17:15:15.402548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:15:15.402548Z digest=sha256:72cdaec47b8ef57070475f61d10b1a99f60348c043faa71c16040aae6af62f46

Observation ec9f7f4e-b96d-498b-a7ca-1c6079c9fd84 · inbound

Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization cites this paper.

Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-04T11:11:02.246586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:11:02.246586Z digest=sha256:d34a0dcdf832c1ff7f45712944cbaf92499ff061c5b053da5e34453baf387b60

Observation fd30f938-bcf4-4fc8-999c-59e096805986 · inbound

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments cites this paper.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-02T20:27:49.725460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:27:49.725460Z digest=sha256:99388b0ea53704c1c0c251a895bd73fbc15c6b8a43624a7f65a98253483e0efa