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Paper Citation Record · LEDGER

Exploring and Mitigating Fawning Hallucinations in Large Language Models

As of 20 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2509.00869.

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

pith.paper-citation-record.v1
2509.00869 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:11:15.502662Z

measured 50 of 50 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T04:47:03.593493Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact2
  • verified fuzzy15
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 12000aeb-81a6-44ff-bf74-e0b109e9d5b6 · outbound

This paper cites GPT-4 Technical Report.

Exploring and Mitigating Fawning Hallucinations in Large Language Models GPT-4 Technical Report

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.178805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.178805Z digest=sha256:1a40f261b1433d0476ada5ae0f963138d284faace3a8d32f135082d5ad6ad531

Observation b7c406fd-7e85-415a-b72c-8d2471fa467e · outbound

This paper cites Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.185693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.185693Z digest=sha256:ad14ff17927dd6b507c8baf96c3fab6ccf149504a339b5759ec9d2db7eb0a9df

Observation e55782d2-0d35-4c3f-aa83-06e7f7905e4b · outbound

This paper cites Improving LLM Abilities in Idiomatic Translation.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Improving LLM Abilities in Idiomatic Translation

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:11:16.470355Z

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.

source=pdf_text observed=2026-08-05T13:11:15.192084Z digest=sha256:560cfb7a28992b34ed77315690af6bf304fd89194c254ae67f5ad256a52cb3fd

Observation 2fdfe76f-ab5e-4f2a-902c-865311d92883 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.199296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.199296Z digest=sha256:e1b801ebac1c92ff8edbe9d7d02b7c1575400640a37c8daf3fbfa470e17b3579

Observation a2297079-c849-4289-b865-eef7ad6666a1 · outbound

This paper cites Ghosh, A.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Ghosh, A

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:17.060457Z

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.

source=pdf_text observed=2026-08-05T13:11:15.207476Z digest=sha256:e847ddbc602fda6ead5492446cb8fccdd61e8de9a908ca75fa4f933ff8a788a8

Observation 49669508-7808-4623-a5d3-c82c0269bbbb · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 6

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:11:16.305953Z

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.

source=pdf_text observed=2026-08-05T13:11:15.212700Z digest=sha256:94e6f973fffcd6546505102ecb20d751ad39218223ae72166ad3a678b8b3844b

Observation 952d9019-742f-4770-8547-e0bcb916b8bc · outbound

This paper cites Shangguan, Y.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Shangguan, Y

Reference 7

Resolution
verified exact
doi, observed 2026-08-05T13:11:15.562764Z

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.

source=pdf_text observed=2026-08-05T13:11:15.219440Z digest=sha256:09e6a431cce9e9dbf6714b28ad403dadb989d6b187f345b30688b19802bcd68a

Observation 0e9982eb-3634-4f43-ad29-c9d008edad7b · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:17.033084Z

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.

source=pdf_text observed=2026-08-05T13:11:15.225493Z digest=sha256:945520b6579f4004ac34c7e0b9927b20cbaf7fc71c0608a364e09ec519d2597c

Observation 2a567e9c-c06e-4aec-b6ae-d652572a03ee · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 9

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:11:16.169292Z

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.

source=pdf_text observed=2026-08-05T13:11:15.230417Z digest=sha256:577859e2090885614000242a619576c4aafae738c9581b7437f88598b1b99db9

Observation 574405ee-3a82-410d-99c2-2a5faab2ccc7 · outbound

This paper cites Why Does ChatGPT Fall Short in Providing Truthful Answers?.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Why Does ChatGPT Fall Short in Providing Truthful Answers?

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.236963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.236963Z digest=sha256:5c05359cc5fc9798579aaaa0a39b6acad68ada1c6a6afa082e1c2a48cadd4dd2

Observation 59bdf2da-de60-4987-b08a-af8d121826f4 · outbound

This paper cites McKenna, T.

Exploring and Mitigating Fawning Hallucinations in Large Language Models McKenna, T

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:17.013733Z

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.

source=pdf_text observed=2026-08-05T13:11:15.244683Z digest=sha256:b96996b8523d87e0d697e3f8ed771d495c63f66d79d5841fbdd540cdda3a1e4d

Observation 04da13a5-cc31-44f9-9f06-710ed1bceb12 · outbound

This paper cites Chuang, Y.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Chuang, Y

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.993394Z

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.

source=pdf_text observed=2026-08-05T13:11:15.252104Z digest=sha256:6c1fe44e1e8d14c387fa773e8f282d85e87e7f9b8a765c1231463da6b4dc632f

Observation 04493e6b-cc69-48be-95ab-b2079c73de36 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.970741Z

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.

source=pdf_text observed=2026-08-05T13:11:15.257450Z digest=sha256:4f32e9985fd994a6e3518681b45422993a2152f30ab449426274d9a447b2fa00

Observation 17548955-5f99-4f31-9326-c96be0775dbe · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.944862Z

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.

source=pdf_text observed=2026-08-05T13:11:15.264561Z digest=sha256:4adfd3ff1f02fff91071bfc815e3d5b9221f12d9135d9451b8cef39b0f07e083

Observation f1ea9c20-692c-4597-9add-02a4768edd0d · outbound

This paper cites Cotra, Why AI alignment could be hard with modern deep learning, Cold Takes (2021).

Exploring and Mitigating Fawning Hallucinations in Large Language Models Cotra, Why AI alignment could be hard with modern deep learning, Cold Takes (2021)

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.925655Z

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.

source=pdf_text observed=2026-08-05T13:11:15.270091Z digest=sha256:b059a8b8f83ce7edb0fe9804a7be44694edbef9af99206f659d98178cdb159cb

Observation 0867dfc3-bc67-4807-803e-2ce22b55c3d6 · outbound

This paper cites Perez, S.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Perez, S

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.904312Z

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.

source=pdf_text observed=2026-08-05T13:11:15.275770Z digest=sha256:988458f37dec66b7dd548740111ed74932fe6209253438e788edf5185d7596ee

Observation 7b95d8c7-2857-4cd7-bd5e-4139eaa2ecc3 · outbound

This paper cites Turpin, J.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Turpin, J

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.881732Z

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.

source=pdf_text observed=2026-08-05T13:11:15.281248Z digest=sha256:e15460d855f0f8d1c861bbcf038b75d5d5d99a364e8edf80bc5b228dba9a6d00

Observation e78d4b64-71b4-495f-ae66-157209157610 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.854284Z

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.

source=pdf_text observed=2026-08-05T13:11:15.286654Z digest=sha256:e6e0fefe10de9797bb35c0a4a35feb68ecc60e1b97db94ad582f18f510a289bc

Observation f63a6e42-55bd-4132-b7cc-02fd452bf85e · outbound

This paper cites Huang, W.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Huang, W

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.829106Z

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.

source=pdf_text observed=2026-08-05T13:11:15.292986Z digest=sha256:be39d47f54654aaa8d20fffc1f0cd97e3738cbbb7508fed2f7c5f66ad3bd729f

Observation ed707feb-75cf-46f7-a91f-cfae926d419a · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.809584Z

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.

source=pdf_text observed=2026-08-05T13:11:15.297858Z digest=sha256:73385960696f1d524de98e2eb50e81ded907fefd84137f022d99c73d01de10e1

Observation 62d29e57-77a4-4266-b55e-84860bcc53fb · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.791057Z

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.

source=pdf_text observed=2026-08-05T13:11:15.304177Z digest=sha256:c218017d46858c45b2fe1d68346569f413b7fd1ca386e11d7c05faf9b1f9b161

Observation 5d23ffdc-870b-4fe1-a6e6-6e3cbc6d9143 · outbound

This paper cites Sharma, M.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Sharma, M

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.773455Z

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.

source=pdf_text observed=2026-08-05T13:11:15.309170Z digest=sha256:1aedc953d4a146e6e2391a030b2bee3ac412d1309dbac7c5c41e93ff68b285a2

Observation f02f1022-4c18-4d22-ab55-f9ef267c6407 · outbound

This paper cites Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.316050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.316050Z digest=sha256:1dad7afb82b9b11d0e7e73de95c6503243732145c8666c71f3fb757282a8f89a

Observation 2b722b44-b4ab-4e5f-a597-dba0ae62aec2 · outbound

This paper cites Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.321455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.321455Z digest=sha256:ba4aa4feec74cb6180ddf123fce64fe0f0c2ed44ee3eb2bff4800ef522a0b18e

Observation b6035dad-8d9a-46f5-901b-c2c4aaf6c4fc · outbound

This paper cites How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts.

Exploring and Mitigating Fawning Hallucinations in Large Language Models How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.327583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.327583Z digest=sha256:2af21d544f5e34d10a5e23b132af46c61377af1b71d56ee4a361fde915ddf17d

Observation eb17d107-5b51-4c73-ba90-081f409605c8 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.334393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.334393Z digest=sha256:7b017bf513de8ad399d26b5ea65188c73fdf2875e49ac43f972417b69895dd9d

Observation 65ffbc35-cccd-4212-a5ce-9a41e96c52b0 · outbound

This paper cites Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.340718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.340718Z digest=sha256:73175ea7a799a7b583890b7aa591cd3680382676a30eb95e116baf64ac2559c6

Observation c627f0f8-a321-417a-9c31-32e8d9eb7092 · outbound

This paper cites Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.347843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.347843Z digest=sha256:7d6e0e1195f45415dde9359925f0a660eac139dbabbd62415d9f4b30d6dc90b7

Observation 00fc1845-1bbb-4acf-ab20-a13dc10b2d74 · outbound

This paper cites Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.354419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.354419Z digest=sha256:4af20f0d40db304ed630a9e7991db824b1cf7cff334d2db2ad67c4b00331b1c8

Observation 58c166c4-cf0e-40a3-b478-70d45c5147b7 · outbound

This paper cites Simple synthetic data reduces sycophancy in large language models.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Simple synthetic data reduces sycophancy in large language models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.361525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.361525Z digest=sha256:c77b206c5e3294c5265a81cc29c39970d8af36876ebfad4a6bc47f5a8534fdbd

Observation 061a86f1-860f-49c2-bc3f-df954a67a01f · outbound

This paper cites From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning.

Exploring and Mitigating Fawning Hallucinations in Large Language Models From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.369956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.369956Z digest=sha256:be14df0ac54115f2ecdc23496d96cde277c35c42de1a42c6c06627fef3e92db6

Observation 9acc8944-353b-4779-934c-6fba6bb46cdf · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.754619Z

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.

source=pdf_text observed=2026-08-05T13:11:15.377113Z digest=sha256:007ff736da268f264d7299d16a598b6813e40c5a72aa3c430b6e46df9147a3a2

Observation 2ed13e14-0892-4b18-a2a6-46c16f8511df · outbound

This paper cites ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding.

Exploring and Mitigating Fawning Hallucinations in Large Language Models ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.385658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.385658Z digest=sha256:244241adc2cbb55c062a8579bf8007fa579466619b2a29c95eba5d5bb2dd15de

Observation a49e8162-9180-4bfb-be70-28aa2f089017 · outbound

This paper cites Contrastive Decoding Improves Reasoning in Large Language Models.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Contrastive Decoding Improves Reasoning in Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.391802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.391802Z digest=sha256:5f9344c64d01480ec0dd0abb1377edf833b2744bab416f636e3828264e1128e3

Observation b7eb2d9a-b2f9-4fef-b2c1-ac086ed6e7f1 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.737092Z

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.

source=pdf_text observed=2026-08-05T13:11:15.398972Z digest=sha256:fb978fa6ee7ee5476ab684335289e71c6a3136febd285b56211fa9847364731c

Observation 725b7862-564c-4f6a-b417-0e9e0cd11131 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.406331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.406331Z digest=sha256:aa1de5da746e7373f8dc215bbcba7ce02595ca1178143389b49430f13b891e05

Observation 01de6c64-e894-431b-96e8-c9ce85c82fca · outbound

This paper cites Mistral 7B.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Mistral 7B

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.411614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 695926d8-68f3-401b-b1c6-1201f47ffdd8 · outbound

This paper cites Qwen3 Technical Report.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Qwen3 Technical Report

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.422816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.422816Z digest=sha256:7e4870b5ebee08c7e8d6b52cc0e6f5a334919c946a2586d2455ae190b5b4da81

Observation 8c9d25f3-246b-47b0-90cc-73cfa8943aaa · outbound

This paper cites The Llama 3 Herd of Models.

Exploring and Mitigating Fawning Hallucinations in Large Language Models The Llama 3 Herd of Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T13:11:15.428296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:11:15.428296Z digest=sha256:1875c2dc91c2136e2d80655bbcba8584b7c09904fe4b7a7bb5500db295fb355c

Observation 7ecdccfa-9c43-4dbe-abb7-efeaab190d96 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.721199Z

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.

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Observation c8173312-ae79-4d52-adb0-d5d2f96cdcc2 · outbound

This paper cites Your task is to accurately answer user questions regarding the sentiment expressed in a given text.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Your task is to accurately answer user questions regarding the sentiment expressed in a given text

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.704839Z

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.

source=pdf_text observed=2026-08-05T13:11:15.441096Z digest=sha256:cd533f29b017069b9951e82d2b1c22c65017e8a82403ec932f5c6eb0b7557dce

Observation 80b08a2c-f4e3-46f6-8903-4b29c140f585 · outbound

This paper cites Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.663300Z

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.

source=pdf_text observed=2026-08-05T13:11:15.459994Z digest=sha256:ecf640a22857723f4cfd284c24b856b15f8eee05b2a3d17a6f96e8cf45dc95bc

Observation c8fae102-b941-41cb-9c1d-d57632c87b09 · outbound

This paper cites neutral",.

Exploring and Mitigating Fawning Hallucinations in Large Language Models neutral",

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.685383Z

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.

source=pdf_text observed=2026-08-05T13:11:15.466817Z digest=sha256:80c3577e77ea21ecc076aa36a2617497434b5a7d11b38d6e539a2a1be1b9a134

Observation 0ac790d8-6f55-4a5a-a8ea-0908aaa80ac3 · outbound

This paper cites Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.642119Z

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.

source=pdf_text observed=2026-08-05T13:11:15.477718Z digest=sha256:8153379e866ce438dc45552606da3ace05e5d4b777d307c824fab26108711dae

Observation e706bdf9-1a03-4b3a-8b39-680b117b2285 · outbound

This paper cites an unresolved cited work.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-05T13:11:16.617917Z

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.

source=pdf_text observed=2026-08-05T13:11:15.486129Z digest=sha256:423651024bddffa61094ca9b489a7655464d09d5217360151a344028514ef485

Observation d8ec2147-a4ca-4d2c-a7fe-ed8b9fa23888 · outbound

This paper cites If there are already details consistent with the ***given label***, incorporate them to strengthen the alignment.

Exploring and Mitigating Fawning Hallucinations in Large Language Models If there are already details consistent with the ***given label***, incorporate them to strengthen the alignment

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.597582Z

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.

source=pdf_text observed=2026-08-05T13:11:15.492491Z digest=sha256:291286b4f3eb2878284fe70d6de94e15284f22e3fb959c18f6ab2f8eeea6865a

Observation 5d6fb663-c8a0-46ce-aff8-28f29dcd6774 · outbound

This paper cites it is said that.

Exploring and Mitigating Fawning Hallucinations in Large Language Models it is said that

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.569094Z

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.

source=pdf_text observed=2026-08-05T13:11:15.497578Z digest=sha256:208223d661893e710cb222370b45f08a0cf161d0e76fdecc10d722d9ed81e108

Observation 6c2a623b-ccd4-4878-90e0-c3c93d7eb5c4 · outbound

This paper cites Under normal prompts, both examples are handled correctly by the base model.

Exploring and Mitigating Fawning Hallucinations in Large Language Models Under normal prompts, both examples are handled correctly by the base model

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:11:16.542487Z

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.

source=pdf_text observed=2026-08-05T13:11:15.502662Z digest=sha256:dc9a4c54675d6883686786e33251152096f6a0b9889cc0ed527b9f9aad273fc0

Pith citing papers

Observation 80e42886-502f-4bad-ac84-a5966fca19bc · inbound

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities cites this paper.

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities Exploring and Mitigating Fawning Hallucinations in Large Language Models

Reference 10

Resolution
malformed identifier
no resolver link, observed 2026-08-02T04:47:03.533883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T04:47:03.533883Z digest=sha256:2cec216eecee9818ebc65dad936b54f3f738d550ba00e89d2b5c3c633455a0d4

Observation caca03fa-bdbc-4036-af0c-31daa2bcebdc · inbound

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities cites this paper.

Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities Exploring and Mitigating Fawning Hallucinations in Large Language Models

Reference 11

Resolution
metadata mismatch
local_arxiv, observed 2026-08-02T04:48:25.418833Z

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.

source=pdf_text observed=2026-08-02T04:47:03.593493Z digest=sha256:db8398548dbe1c2f5132b4a17c7375b5fe09e42fb5347ea376c3744048b85313