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Paper Citation Record · LEDGER

Exploring and Mitigating Fawning Hallucinations in Large Language Models

As of 13 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 2026-08-13T06:32:02.005865+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:f96bcac6049726742890e5e7166b816af400250ef21455f42db6690388bdc64a

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:dd35260d12463cdc53a8dd0376dab53229a7f017f00c68e9360c1bb4174af150

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.192084Z digest=sha256:37802bdabdb0cc2d8655ea0ec77b0e4e84318cb180b0e8fecff33b9663e60c86

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=sha256:54baeb40ffe12d34487b2788ad5e82563232689ade9a945926ee81ee555841db

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.207476Z digest=sha256:aba87fc3dfaef9dc26279e5b90f400636b7a7c41b4e09d78e3f6f35512c22a39

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.212700Z digest=sha256:adf6acd4d59892245f5d0d351a2082510116f117dc1e7316288c26a179ec0b60

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.219440Z digest=sha256:6f5bd52d434a15eea5171f8e5c9be758be5a3641010c9b2ed6499c45199bd31f

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.225493Z digest=sha256:89e2e46a203bd8507632a75ca17765f5dc884504cce7b6c1bd7256b8b6d0e8c4

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.230417Z digest=sha256:ad0fb636f5106b738346d6c24bfbb85f0d2f0e91464db2a9511ca9a624f43a87

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=sha256:66e1c27165fbf8c55d663348152a397f28867025f17e2052862f789f2a4d0461

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.244683Z digest=sha256:32962271a3a4baad659631fa30c278747e12dc3407a5b6e5d1f1f8be56c40981

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.252104Z digest=sha256:cc33016e44e7577e57b6bae5fc4d810686fdba3745473e82326f595b1a87e0af

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.257450Z digest=sha256:a4814ed255c6c92ffb6d1a3356f1b6b5f4761d6b1fa633be129e55e3d8fdb3e8

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.264561Z digest=sha256:6935150f176296b84e7702d4bb3270a5e3aea301dfb5e0286c9f77eb6d6fbec5

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.270091Z digest=sha256:116d7eb0fa157f4250d40cdf039c8b154277024801e9e6f69a2061242d821521

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.275770Z digest=sha256:51b753012a2af5070909af3d87dd0edf4bcb28b28a9e646de5d733f4711ddcf0

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.281248Z digest=sha256:16cc62f17aeb1ce9dbe4f84548b2de56fed3ea7804141f706e51ff409f45c281

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.286654Z digest=sha256:9a1e6ef340ce646e1246d052f32cdfdcab58418e2cd2c023f3e7d416b87d7a33

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.292986Z digest=sha256:3a7063bf2343c01977be5b3f0a04b4ee4598f3b7adfd1faa8a03f95f1d1c75be

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.297858Z digest=sha256:67ff6bc4cdb6e0bcbd51232d842c20fa553a57bf340502cf85ab19ae90dfa1ba

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.304177Z digest=sha256:e821e7ba449fafdd5d59bd1ad94fcfaf4abbfdd3c7ae1439b7b28e0f14a97b95

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.309170Z digest=sha256:9e6e79e4d2264ef9eef041e203751fc4167fa0baf872dc291fda0986ddc01aa9

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:daf6abda0f8f2270a7338648ad36b0aec32fe46cd553a33031840c047ac4c1fa

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=sha256:5feed428aa9d142a91116af6f6c92939fd7a941fa616d96c077f8b8bf5c3389e

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=sha256:8be250cd24d52faa58528e651d9c1eeac4a71d6daf46b393fb0ba0034d2fce83

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=sha256:1567d12c290ff235c4663db8e7f141b5ece1945987ed8695590a92f156a2f60e

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:c0f2ad09db52a92e682ca2baf9e3c2644e56723e99419d8d8861bf17a5ae1593

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=sha256:2a21f5e35c16679c34eebd089914f1687a0e9d798fc5c6d8dfcaf42f05c2686f

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=sha256:6bdd68b4e3f13011157a2039ef66ebe70a078359de6f19622bed489ed33d6a0e

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=sha256:9a821cb3abf85c2ee5e33ea8355a210beeb1f6bbc4bbf9b08ae200c8b31b520d

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=sha256:213889b4ecfc7a510077530e1146ac222af89f75f031a19acc4e5a51e79f26d2

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.377113Z digest=sha256:393bbea19f1270e0d416b5af253c8b258d40107bd453f1010670db9eae939b68

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:ecadbd48026ca8534161b603e455669a34a1cfcb563f74b458e3f402e9938dd3

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:be2223d0d4f68df14ba5e38d976de259d0465ad1c003982f252c19db4ca89e9e

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.398972Z digest=sha256:1611fdac1da6808d0939668dfc88f1c0fc766166a8b065a9d461654c2a52b3dc

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=sha256:3e3452c965dd94b1e26990c4ae176cd96e8b211fae90050ccb8fbf1a4b5b41e1

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=sha256:42b556670e8b3b7e8052fc852063eabd197b8c7f4a6f28c06b319fccb65387fc

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=sha256:26c7d1e7e1395921c6298a03238888d8e1418af1504fc96ae1767f81aa043eaf

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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.441096Z digest=sha256:b82256a4e65ab271d9fceeb6a5261c6bc1616171947bac5bacb9d03b84884d0f

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 2026-08-13T06:32:02.005865+00:00.

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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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.466817Z digest=sha256:c5bcd5b6de1d5b90593044360ff97486c75236a471b5e6c99bd1df65da114507

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.477718Z digest=sha256:2619a733a73c180a87902e27ccc9c33dc6455e81f1cb09eacad05bdd3cc8d194

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.486129Z digest=sha256:fe02d7a7066965ec81c58ea6c7d78637e7cb624c899a2b6c9153f4732562e9e1

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.492491Z digest=sha256:7e446ca0d2095cfa24a4d2f86557bda96c17f1f01c212338189dcde4f0b6e039

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.497578Z digest=sha256:7c2e3c5708b76df992b5c6290734c635ee084b7c9ae6a39385e8ca21fc4c8f2f

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:11:15.502662Z digest=sha256:b1ec948f6784466e3bd6e2687789c197787eead1ac115286ba33ea5d2f871e90

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=sha256:62715d3271a04e0189d796aa224c6198b65fdda0cccb62d883ac92320c5c2179

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-02T04:47:03.593493Z digest=sha256:7638121e908e4617cfef6c3899a981996a90d2ece7be9794026971d64f819b2e