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Paper Citation Record · LEDGER

Theoretical Foundations and Mitigation of Hallucination in Large Language Models

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2507.22915.

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

pith.paper-citation-record.v1
2507.22915 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:48:46.005378Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:53:13.366975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T05:53:59.401008Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy33
  • unresolved3
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  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81e3c717-a23f-4723-ba68-953b0245f64c · outbound

This paper cites Attention is all you need,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Attention is all you need,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation a8b59a1a-bc0f-4af7-a077-f19461c36eb2 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models BERT: Pre-training of deep bidirectional transformers for language understanding,

Reference 2

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation fe72eca7-a9b7-47f5-89e0-205913b21098 · outbound

This paper cites Language models are few-shot learners,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Language models are few-shot learners,

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation c16cc9ff-520d-4825-bc9f-f26d2d3d5367 · outbound

This paper cites Survey of hallucination in natural language generation,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Survey of hallucination in natural language generation,

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 16a4cd1e-bf0b-4618-8403-790824997e28 · outbound

This paper cites On faithfulness and factuality in abstractive summarization,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models On faithfulness and factuality in abstractive summarization,

Reference 5

Resolution
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Source-reported events for the cited work

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Observation 71bb127d-a735-4b99-97a1-f5a282285d73 · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Detecting hallucinations in large language models using semantic entropy,

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation f6c921e6-c18b-4933-a0f8-0fbe2a6f584c · outbound

This paper cites A Neural Conversational Model.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models A Neural Conversational Model

Reference 7

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation edaff2e7-3682-4913-b4c4-f5605b2563ed · outbound

This paper cites Six challenges for neural machine transla- tion,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Six challenges for neural machine transla- tion,

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9103521b-02f8-40cb-9ce6-91fae5a4a9e2 · outbound

This paper cites The curious case of hallucinations in neural machine translation,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models The curious case of hallucinations in neural machine translation,

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3d675ae4-ed06-4837-90e0-3a825500c14f · outbound

This paper cites Information Technology Needs in Vehicle Dynamics Control.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Information Technology Needs in Vehicle Dynamics Control

Reference 10

Resolution
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Source-reported events for the cited work

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Observation c0f579e5-d557-43da-bc3d-d0914ef3e951 · outbound

This paper cites Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

Reference 11

Resolution
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Source-reported events for the cited work

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Observation 54cbb8a8-9c6b-42ec-8915-dcc6da450144 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 12

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 77fe2926-6844-4ac7-9145-3fbfa0af55f3 · outbound

This paper cites Language models (mostly) know what they know,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Language models (mostly) know what they know,

Reference 13

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Source-reported events for the cited work

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Observation c4651c91-21e6-43b9-b14c-c09b92d3c8df · outbound

This paper cites On calibration of modern neural networks,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models On calibration of modern neural networks,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f65450d5-7358-4bf5-b834-b970ae865326 · outbound

This paper cites How can we know when language models know? On the calibration of language models for question answering,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models How can we know when language models know? On the calibration of language models for question answering,

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 7cb05a51-fb8c-4412-8020-280285fd3b71 · outbound

This paper cites Get to the point: Summarization with pointer-generator networks,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Get to the point: Summarization with pointer-generator networks,

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation e6f0b987-5831-43e5-87b2-a88f4ee0e940 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive NLP,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Retrieval-augmented generation for knowledge-intensive NLP,

Reference 17

Resolution
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Source-reported events for the cited work

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Observation 0f4c34bd-9d25-43b6-b52b-5be4085c38e2 · outbound

This paper cites REALM: Retrieval-augmented language model pre-training,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models REALM: Retrieval-augmented language model pre-training,

Reference 18

Resolution
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Source-reported events for the cited work

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Observation 9a0b150f-3378-454b-83bd-47bece8b155e · outbound

This paper cites Knowledge-informed dialogue generation,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Knowledge-informed dialogue generation,

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 11ecd777-811a-4ea0-8add-8befe7f3fbe4 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Training language models to follow instructions with human feedback,

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation afc64da3-d2b5-4671-9f04-4f4c4a532eef · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models WebGPT: Browser-assisted question-answering with human feedback

Reference 21

Resolution
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Source-reported events for the cited work

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Observation 58964010-c3c9-473e-8bc6-7f7dfd2e1e4d · outbound

This paper cites Calibration of pre-trained transformers,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Calibration of pre-trained transformers,

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 16b1edaf-4a65-43a3-b923-7947a7e42815 · outbound

This paper cites The curious case of neural text degeneration,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models The curious case of neural text degeneration,

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f12f6ffe-ea7e-42ea-9685-dc12a5296030 · outbound

This paper cites Evaluating the factual consistency of abstractive text summarization,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Evaluating the factual consistency of abstractive text summarization,

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 472ceaf7-7148-4e68-a87e-9e7995697549 · outbound

This paper cites SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models,

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e95eea1a-9882-4503-9abd-9fc5acbabbcc · outbound

This paper cites Asking and answering questions to evaluate the factual consistency of summaries,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Asking and answering questions to evaluate the factual consistency of summaries,

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f71bbe72-d884-48fb-966c-d1c7f3218609 · outbound

This paper cites TRUE: Re-evaluating factual consistency evaluation,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models TRUE: Re-evaluating factual consistency evaluation,

Reference 27

Resolution
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Source-reported events for the cited work

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Observation dab26b32-bbbd-49a5-99ea-bf4a6ffe6655 · outbound

This paper cites TruthfulQA: Measuring how models mimic human falsehoods,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models TruthfulQA: Measuring how models mimic human falsehoods,

Reference 28

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 56848319-4e37-4c47-b6df-25dc08a9997f · outbound

This paper cites Understanding fac- tuality in abstractive summarization with FRANK: A benchmark for factuality metrics,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Understanding fac- tuality in abstractive summarization with FRANK: A benchmark for factuality metrics,

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6562e181-e194-4cb1-bb3a-29db7e56be50 · outbound

This paper cites Knowledge-based metrics for dialogue: Unifying structured and unstructured knowledge,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Knowledge-based metrics for dialogue: Unifying structured and unstructured knowledge,

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation f7deae15-4f7d-43c3-ad5a-72776e1c47dd · outbound

This paper cites FEVER: a large-scale dataset for fact extraction and verification,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models FEVER: a large-scale dataset for fact extraction and verification,

Reference 31

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation f56604e3-a0e4-407e-b5ed-3ef5a1c6d9d6 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models What uncertainties do we need in bayesian deep learning for computer vision?

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e6cc4718-1fb0-4c39-b0a4-d93732f3b951 · outbound

This paper cites Selective question answering under domain shift,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Selective question answering under domain shift,

Reference 33

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1eb3b0c8-7b94-4806-9c79-fdfcb6614c71 · outbound

This paper cites Chain-of-Verification Reduces Hallucination in Large Language Models.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Chain-of-Verification Reduces Hallucination in Large Language Models

Reference 34

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Source-reported events for the cited work

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Observation e034d2ee-3bce-4578-b80f-bfe6251e50b5 · outbound

This paper cites Mitigating hallucination by integrating knowledge graphs into LLMs,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Mitigating hallucination by integrating knowledge graphs into LLMs,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:48:47.570611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 42881737-a8c1-483e-afca-4172a4906a60 · outbound

This paper cites Rademacher and gaussian complexi- ties: Risk bounds and structural results,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Rademacher and gaussian complexi- ties: Risk bounds and structural results,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:48:47.327796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:48:45.768719Z digest=sha256:f39a5c41a9ed134eb55444408449e6be8cc9dce8f9e1ce2625563073dc4d392f

Observation 0f574300-8943-4569-a9b3-0a8b501600d7 · outbound

This paper cites Vapnik, Statistical Learning Theory.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models Vapnik, Statistical Learning Theory

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:48:47.014546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9bdfb07e-59d1-4c94-9705-30e7b7158f0f · outbound

This paper cites PAC-Bayesian model averaging,.

Theoretical Foundations and Mitigation of Hallucination in Large Language Models PAC-Bayesian model averaging,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:48:46.739884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

Observation 10aca59e-b0ab-4a24-a9fb-d9fe9988f6a7 · inbound

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE cites this paper.

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE Theoretical Foundations and Mitigation of Hallucination in Large Language Models

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-05T10:53:13.366975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:53:13.366975Z digest=sha256:2ec87fad2d0a6da2e2e7ba41a7f34c6e0f4757d06ea075ee99638ec8e1057232

Observation 2098c569-4573-4818-b4e6-06faf540498b · inbound

Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing cites this paper.

Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing Theoretical Foundations and Mitigation of Hallucination in Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T06:03:02.837307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:03:02.837307Z digest=sha256:693c3d5e513bd92726681bd2726f1ddc736afad25ae705eb245cfa4fbc4b415d

Observation 0e7d4ce6-b1c9-4621-afc2-913ffc482f25 · inbound

Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models cites this paper.

Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models Theoretical Foundations and Mitigation of Hallucination in Large Language Models

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:53:59.402734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T05:49:55.738870Z digest=sha256:5932ac4bad68795922873ba584d77f8452a9d1b094a141ef7638e6ccaab67f69