Paper Citation Record · LEDGER

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2505.02362.

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

pith.paper-citation-record.v1
2505.02362 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:56:58.027047Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:41:57.765396Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact1
verified fuzzy9
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3b2c181-994f-4143-b2ce-be324544afc3 · outbound

This paper cites Explainability with semantic concept composition and zero- shot learning for anomaly detection,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Explainability with semantic concept composition and zero- shot learning for anomaly detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.136067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76100dcd-a31c-4c14-b1e5-f0fd2b618127 · outbound

This paper cites Survey of review spam detection using machine learning techniques,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Survey of review spam detection using machine learning techniques,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.129521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f88e6e37-640a-4dce-aa20-b60f78f1968b · outbound

This paper cites Applying lazy learning algorithms to tackle concept drift in spam filtering,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Applying lazy learning algorithms to tackle concept drift in spam filtering,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.122114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e04a2d80-ec56-4eba-8ce2-f27485357999 · outbound

This paper cites ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T00:56:58.002825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b8c5d4-7009-468b-bca6-25b8a70c85a5 · outbound

This paper cites SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T00:56:58.005683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3eb2133-f2ca-417e-85f7-c9d03f2c20c2 · outbound

This paper cites Prompt-learning and zero-shot text classification with domain- specific textual data,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Prompt-learning and zero-shot text classification with domain- specific textual data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.114023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5df3d9c-9065-4478-b4a1-4b4febc8dc3b · outbound

This paper cites Deceptive opinion spam detection approaches: a literature survey,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Deceptive opinion spam detection approaches: a literature survey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.106245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb3b6e07-a47e-4878-a67b-0c418c50f19b · outbound

This paper cites A review on social spam detec- tion: Challenges, open issues, and future directions,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models A review on social spam detec- tion: Challenges, open issues, and future directions,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.099150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bba7c73-1ba5-4f8c-ae7f-381030891ed4 · outbound

This paper cites Evaluating the Performance of ChatGPT for Spam Email Detection.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Evaluating the Performance of ChatGPT for Spam Email Detection

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:56:58.056562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 925bcbcc-b115-4e35-a83c-eb8349c96b04 · outbound

This paper cites Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T00:56:58.018898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d57b8e9-4f49-4dba-a8ae-0c7d27ee1628 · outbound

This paper cites Machine learning for spam detec- tion,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Machine learning for spam detec- tion,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.091880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1cc7f9d-c2c2-49af-adba-c48919cd4922 · outbound

This paper cites Chatgpt: A threat to spam filtering systems,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Chatgpt: A threat to spam filtering systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.084173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ecea1c49-5fb2-422b-a76d-2ecbf9189094 · outbound

This paper cites Semantics-aware bert for language understanding,.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models Semantics-aware bert for language understanding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:56:58.076865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation eadea79c-05a2-4dd6-b589-cb9341797b5d · inbound

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data cites this paper.

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T00:41:57.765396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.