Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:11:38.449070Z

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 2026-08-07T06:34:17.273281+00:00.

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.

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.

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.

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.

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.

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.

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.