Paper Citation Record · LEDGER

Improving Phishing Email Detection Performance of Small Large Language Models

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2505.00034.

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

pith.paper-citation-record.v1
2505.00034 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:23:34.773909Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

29 of 29 outbound references displayed

verified exact2
verified fuzzy20
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f0e1baf-4a03-4c27-b6c1-197dffa9a065 · outbound

This paper cites A bayesian approach to filtering junk e-mail.

Improving Phishing Email Detection Performance of Small Large Language Models A bayesian approach to filtering junk e-mail

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.864504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be770e5c-00c5-4df2-93fc-59170033db79 · outbound

This paper cites Drucker, Donghui Wu, and V .N.

Improving Phishing Email Detection Performance of Small Large Language Models Drucker, Donghui Wu, and V .N

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.851145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80d7f587-2302-4675-bc19-58455bbf912c · outbound

This paper cites A comparison of machine learning techniques for phishing detection.

Improving Phishing Email Detection Performance of Small Large Language Models A comparison of machine learning techniques for phishing detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.795028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b75c4af9-ce39-419a-9641-31fc921930b4 · outbound

This paper cites Deep learning for phishing detection: Taxonomy, current challenges and future directions.

Improving Phishing Email Detection Performance of Small Large Language Models Deep learning for phishing detection: Taxonomy, current challenges and future directions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.618447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eeb69409-80cf-43d1-a5fa-b3ab941eb669 · outbound

This paper cites Salinas Monroy.

Improving Phishing Email Detection Performance of Small Large Language Models Salinas Monroy

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.569218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77be77e3-8125-4012-844f-6da5beb7af80 · outbound

This paper cites Balachander.

Improving Phishing Email Detection Performance of Small Large Language Models Balachander

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.464904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59865a2d-331c-4bac-9b5d-b8844dfcfbe1 · outbound

This paper cites Attention is all you need.

Improving Phishing Email Detection Performance of Small Large Language Models Attention is all you need

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.395849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae2c66d5-0b7f-41a9-8065-2a539184fb21 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Improving Phishing Email Detection Performance of Small Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.398749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df14d5a3-a228-4abb-af90-b37d0c93df94 · outbound

This paper cites Introducing ChatGPT, https://openai.com/index/chatgpt, 2022.

Improving Phishing Email Detection Performance of Small Large Language Models Introducing ChatGPT, https://openai.com/index/chatgpt, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.446374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bdf8a3db-d02c-4c14-bf26-f104a0d642cb · outbound

This paper cites GPT-4, https://openai.com/index/gpt-4/, 2022.

Improving Phishing Email Detection Performance of Small Large Language Models GPT-4, https://openai.com/index/gpt-4/, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.435972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df0b8b1c-b247-461b-94a1-2e927040b8a1 · outbound

This paper cites Debate-driven multi-agent llms for phishing email detection.

Improving Phishing Email Detection Performance of Small Large Language Models Debate-driven multi-agent llms for phishing email detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.425867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e3d88cf-ff59-4ce3-9a6c-30b21306ac93 · outbound

This paper cites Phishing email dataset, https://www.kaggle.com/datasets/naserabdullahalam/phishing- email-dataset, 2024.

Improving Phishing Email Detection Performance of Small Large Language Models Phishing email dataset, https://www.kaggle.com/datasets/naserabdullahalam/phishing- email-dataset, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.414924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad723280-9496-47d1-9eea-4e1ab178170e · outbound

This paper cites Introducing Llama 3.1, https://ai.meta.com/blog/meta-llama-3-1, 2024.

Improving Phishing Email Detection Performance of Small Large Language Models Introducing Llama 3.1, https://ai.meta.com/blog/meta-llama-3-1, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.373293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b1287b93-b6c0-439f-ab79-cb838134c968 · outbound

This paper cites Llama 3.2, https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices, 2024.

Improving Phishing Email Detection Performance of Small Large Language Models Llama 3.2, https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.259400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 845db953-38db-4605-85ed-0afcbc911c77 · outbound

This paper cites Phi-4-mini technical report: Compact yet powerful multimodal language models via mixture-of-loras.

Improving Phishing Email Detection Performance of Small Large Language Models Phi-4-mini technical report: Compact yet powerful multimodal language models via mixture-of-loras

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.247569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b4b3c0ba-cada-4600-bff5-178c632f741e · outbound

This paper cites GPT-4o-mini, https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence, 2024.

Improving Phishing Email Detection Performance of Small Large Language Models GPT-4o-mini, https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.234252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f722117a-27a2-483a-8681-8d130505d6d7 · outbound

This paper cites An experi- mental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages.

Improving Phishing Email Detection Performance of Small Large Language Models An experi- mental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.222121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b322b05-e0fd-41e9-aba0-ab1b6d348853 · outbound

This paper cites An evaluation of Naive Bayesian anti-spam filtering.

Improving Phishing Email Detection Performance of Small Large Language Models An evaluation of Naive Bayesian anti-spam filtering

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:23:34.912946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d1973698-4592-4fe8-a876-88a62b586734 · outbound

This paper cites Support vector machines for spam categorization.

Improving Phishing Email Detection Performance of Small Large Language Models Support vector machines for spam categorization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.130260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8645cdb6-19da-4762-ab3d-58b9662d3d62 · outbound

This paper cites Boosting Trees for Anti-Spam Email Filtering.

Improving Phishing Email Detection Performance of Small Large Language Models Boosting Trees for Anti-Spam Email Filtering

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:23:34.897305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec0e318a-cfbc-49c3-8ca4-dd0bf5117d0b · outbound

This paper cites Deep learning to filter sms spam.

Improving Phishing Email Detection Performance of Small Large Language Models Deep learning to filter sms spam

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.037001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ab50c3b-af3c-4f2b-a24c-14c5b45ad004 · outbound

This paper cites Spam detection using bidirectional transformers and machine learning classifier algorithms.

Improving Phishing Email Detection Performance of Small Large Language Models Spam detection using bidirectional transformers and machine learning classifier algorithms

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.025668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ba7e049-2b23-4a51-876b-64b0932ff27c · outbound

This paper cites A thorough benchmark of automatic text classification: From traditional approaches to large language models.

Improving Phishing Email Detection Performance of Small Large Language Models A thorough benchmark of automatic text classification: From traditional approaches to large language models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.751644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a38c8ab-15fe-454f-b706-831ca881551e · outbound

This paper cites Devising and detecting phishing emails using large language models.

Improving Phishing Email Detection Performance of Small Large Language Models Devising and detecting phishing emails using large language models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.755925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8326942e-a205-4345-a0a4-96d24277ff8d · outbound

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

Improving Phishing Email Detection Performance of Small Large Language Models ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.759610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf6aefac-f501-47e5-a3ca-11e88193db03 · outbound

This paper cites Improving language understanding by generative pre-training.

Improving Phishing Email Detection Performance of Small Large Language Models Improving language understanding by generative pre-training

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.763363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 770c4240-e779-4a69-92e5-421ea7385498 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen.

Improving Phishing Email Detection Performance of Small Large Language Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:35.002581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6455fefb-77ae-4d64-8677-bd193b0f0a18 · outbound

This paper cites Introducing Qwen, https://qwenlm.github.io/blog/qwen, 2024.

Improving Phishing Email Detection Performance of Small Large Language Models Introducing Qwen, https://qwenlm.github.io/blog/qwen, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:23:34.957524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5176d6ab-c1d4-40dd-8999-a2257fcd2164 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Improving Phishing Email Detection Performance of Small Large Language Models Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:34.773909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.