Paper Citation Record · LEDGER

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification

As of 14 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2411.11247.

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

pith.paper-citation-record.v1
2411.11247 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:49:10.071272Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy6
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a9a349de-e147-41d8-b2ea-5007e3c9c758 · outbound

This paper cites GPT-4 Technical Report.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.912781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b214bc0c-7dc4-4e37-8027-1b891b7efc1b · outbound

This paper cites In: Proceedings of the Fourth Workshop on Fact Ex- traction and VERification (FEVER).

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Proceedings of the Fourth Workshop on Fact Ex- traction and VERification (FEVER)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.675039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f603ce19-ce3b-445e-adef-c7853b8a735e · outbound

This paper cites an unresolved cited work.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:49:10.642053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e154d81b-4b6f-4245-aa59-ac4aa960cb70 · outbound

This paper cites Are Large Language Models Good Fact Checkers: A Preliminary Study.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Are Large Language Models Good Fact Checkers: A Preliminary Study

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.938081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5281f4-5c6e-4956-81f5-2a513e2f83f7 · outbound

This paper cites Information Re-Organization Improves Reasoning in Large Language Models.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Information Re-Organization Improves Reasoning in Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.952165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a540c6a-e332-49db-8e02-0c7fbca257df · outbound

This paper cites In: Proceedings of the 2019 Conference on Empirical MethodsinNatural LanguageProcessing andthe9th InternationalJointConferenceon Natural Language Processing (EMNLP-IJCNLP).

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Proceedings of the 2019 Conference on Empirical MethodsinNatural LanguageProcessing andthe9th InternationalJointConferenceon Natural Language Processing (EMNLP-IJCNLP)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.620453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eddc079b-d183-4145-8d06-3752df5bf408 · outbound

This paper cites Transactions of the Association for Computational Linguistics10, 178–206 (2022).

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Transactions of the Association for Computational Linguistics10, 178–206 (2022)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.967983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da909198-4210-44af-a809-0ca21892f5b9 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.975017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55d9d6f7-02b3-4c59-9bad-1eb8e152765e · outbound

This paper cites HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:09.988540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68312d13-73a0-4ba7-a093-b46f55f69bf7 · outbound

This paper cites In: Findings of the Association for Computational Linguistics.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Findings of the Association for Computational Linguistics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.572956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e491d6e3-6cc7-4337-bdd5-34974914fdc8 · outbound

This paper cites Large Language Models: A Survey.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Large Language Models: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:10.010697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47417845-fa78-47bc-99f5-0c386d5ba3e2 · outbound

This paper cites In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.548461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1675c91a-e6b0-403b-b007-550fcbae799c · outbound

This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing: System Demonstrations.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Proceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing: System Demonstrations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.526329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51ce8e15-ce09-4d45-8f95-2308d4d593ce · outbound

This paper cites In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:49:10.500921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f78c116c-7a5b-4cc5-a871-32288818b4ba · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification LLaMA: Open and Efficient Foundation Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:10.062952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1539fa64-d977-42eb-8369-af91f254e3d8 · outbound

This paper cites Advances in neural information processing systems35, 24824–24837 (2022).

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Advances in neural information processing systems35, 24824–24837 (2022)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T18:49:10.071272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.