Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2508.16757.

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

pith.paper-citation-record.v1
2508.16757 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:15:15.418227Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:27:45.962398Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

verified exact3
verified fuzzy1
unresolved49
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfa1163e-f440-481a-8aed-1c3d324bc3c3 · outbound

This paper cites RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:15:15.986360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23bd47cb-e809-4cfb-8e98-82f7510790ba · outbound

This paper cites Generator-Retriever-Generator Approach for Open-Domain Question Answering.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Generator-Retriever-Generator Approach for Open-Domain Question Answering

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.218967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0091b7ee-df6a-4a8b-87b6-025d1e210d23 · outbound

This paper cites Abdelgwad, and Adam Jatowt.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Abdelgwad, and Adam Jatowt

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:15:16.216329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 54aa36f5-8d2d-431a-a00d-d2f89cff084f · outbound

This paper cites ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:15:15.949456Z

Source-reported events for the cited work

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Observation 7213cb4a-6e40-4fba-8859-f7306b185329 · outbound

This paper cites Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.232104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8089e7b7-b184-4a6b-80d2-2e29719e774c · outbound

This paper cites TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.236419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ac80b61-252d-4050-b95f-b1f671da9f70 · outbound

This paper cites GPT-4 Technical Report.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models GPT-4 Technical Report

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.240952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31aa87bd-e47b-4849-bbc4-81f1d4323bb4 · outbound

This paper cites InPars: Data Augmentation for Information Retrieval using Large Language Models.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models InPars: Data Augmentation for Information Retrieval using Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.244567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e01a550-a841-47aa-89f4-01b7870c4aa3 · outbound

This paper cites Reading Wikipedia to Answer Open-Domain Questions.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Reading Wikipedia to Answer Open-Domain Questions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.248431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05841dfe-128f-40fb-8cb4-7837ea77ed7c · outbound

This paper cites Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.252429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ffb1c9a-8245-4a0b-bcec-aef4e54cfadc · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation f863deb5-4b7b-432d-a8f3-bad916be3ef6 · outbound

This paper cites Overview of the TREC 2019 deep learning track.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Overview of the TREC 2019 deep learning track

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.260444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77276f47-a299-416c-b4f7-ba76b150ad97 · outbound

This paper cites Calibration of Pre-trained Transformers.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Calibration of Pre-trained Transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.264309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c892fb2f-fef9-4940-93fd-8bd5e4b05bdd · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.268059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942fec91-8a57-4fbe-8680-c55f2cc687e8 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.271643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62cde85a-f37c-4ac6-b554-5a848a051b02 · outbound

This paper cites ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.275605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a88aacd8-5d3b-4c5d-b1d6-7c35231fde31 · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.279206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6f757f4-4a98-44fe-a810-32d5771a962a · outbound

This paper cites LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.283254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c9bcc95-5c4a-430f-ae64-403988a1b2d9 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.287461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de076d88-154b-45ad-843a-3b8699de1ba7 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.291076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ad41e8-1f04-46e6-8722-87f661da9613 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8426a61a-f140-4c58-acb1-854efb905654 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 22

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Unavailable: canonical work link unavailable.

Observation dd6aacdb-2d54-4a43-95c7-6f3f05ad92b7 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:15:16.135770Z

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Observation a31b9590-6392-410e-9e66-c77f1f0b98e6 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 24

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Observation b03c0e8c-77d2-484a-ac7c-6fd8d1b78cf1 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.310224Z

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Observation a4f180b9-8387-4fef-a57c-b3d0f102b746 · outbound

This paper cites Zero-Shot Listwise Document Reranking with a Large Language Model.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Zero-Shot Listwise Document Reranking with a Large Language Model

Reference 26

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Observation 9f5e0e86-2e0f-4747-89b9-79f171d125f2 · outbound

This paper cites RaFe: Ranking Feedback Improves Query Rewriting for RAG.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models RaFe: Ranking Feedback Improves Query Rewriting for RAG

Reference 27

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Observation 6f21cfb0-fbc8-4123-ad5d-c23b3c2d827d · outbound

This paper cites Exploring Hint Generation Approaches in Open-Domain Question Answering.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Exploring Hint Generation Approaches in Open-Domain Question Answering

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:15:15.718703Z

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Observation 8db80740-a756-44dc-9abf-01a303ffdb1c · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 29

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Observation cfbd882d-01d6-40ea-820a-e161e0b2d502 · outbound

This paper cites Passage Re-ranking with BERT.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Passage Re-ranking with BERT

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.329225Z

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Observation a07b3e0e-c406-4a0f-aa7f-7b44a461ea40 · outbound

This paper cites Document Ranking with a Pretrained Sequence-to-Sequence Model.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Document Ranking with a Pretrained Sequence-to-Sequence Model

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.333064Z

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Observation 863a2112-37d2-4c06-8dec-bd05cf731058 · outbound

This paper cites RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models

Reference 32

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Observation 7b8c2880-0d3a-4bb2-9f71-4bdd82d095f2 · outbound

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

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

Reference 33

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Observation b3c644a5-8c76-42e3-8592-f47a90a4098b · outbound

This paper cites Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

Reference 34

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Observation 7bf3b6e5-7fdc-4612-8616-f1aa84875851 · outbound

This paper cites EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models

Reference 35

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Observation 75dd6802-1c72-4b8e-8606-491611149dea · outbound

This paper cites FIRST: Faster Improved Listwise Reranking with Single Token Decoding.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models FIRST: Faster Improved Listwise Reranking with Single Token Decoding

Reference 36

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Observation f71f28c1-f1a6-4e61-ab41-3cdce5021d4b · outbound

This paper cites Improving Passage Retrieval with Zero-Shot Question Generation.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Improving Passage Retrieval with Zero-Shot Question Generation

Reference 37

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Observation 300c1050-3903-4cd8-a3d5-dc7bee20849e · outbound

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How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 38

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Observation b22c933a-d9f4-4024-8c0f-bf3f1665e240 · outbound

This paper cites Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents

Reference 39

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Observation 4ce76377-8243-45a1-aa11-147c6d5c2fd0 · outbound

This paper cites Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models

Reference 40

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Observation c00d3269-cc29-42a2-83d0-48e167ea6f91 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 41

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Observation 541b3d8c-4e6f-4aff-bfd4-9d8b13b59ff5 · outbound

This paper cites Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers

Reference 42

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Observation 4f6309a1-76d3-4938-86b5-2c7f69e84060 · outbound

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How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:15:16.066691Z

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Observation 7e34c48b-04aa-41d8-ac88-5faa755c050a · outbound

This paper cites ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

Reference 44

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Observation a07d5f22-9a29-422a-af58-ffd4a220a242 · outbound

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How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 45

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Observation 47abec7d-6c94-45f0-9274-fff2a78206eb · outbound

This paper cites Generate rather than Retrieve: Large Language Models are Strong Context Generators.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.391228Z

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Observation ee1e32b8-0349-492d-be3e-5dbd0034c27c · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:15:16.051641Z

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Observation 6b9a6a77-7eb3-4206-af5b-52b8f33acfd8 · outbound

This paper cites Retrieval-Augmented Generation for AI-Generated Content: A Survey.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 48

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

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

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

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Observation 1639bbfe-9ead-4b95-a795-289404d194c3 · outbound

This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:15:16.036075Z

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This paper cites an unresolved cited work.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Unresolved cited work

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This paper cites online" 'onlinestring :=.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models online" 'onlinestring :=

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T17:15:15.414061Z

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This paper cites write newline.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models write newline

Reference 53

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Pith citing papers

Observation aa2ebf22-f909-4802-990e-d4fe29397e0d · 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 How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T20:27:45.962398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.