Paper Citation Record · LEDGER

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2509.07190.

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

pith.paper-citation-record.v1
2509.07190 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:45:42.655050Z

measured 47 of 47 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 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

47 of 47 outbound references displayed

verified exact0
verified fuzzy34
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 044be7a5-020d-47ea-8d8a-21ac7366447b · outbound

This paper cites Assessing LLMs in malicious code deobfuscation of real-world malware campaigns,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Assessing LLMs in malicious code deobfuscation of real-world malware campaigns,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.189805Z

Source-reported events for the cited work

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

Observation 297995e4-140c-4d04-813c-487dc924daf8 · outbound

This paper cites Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T22:45:42.486029Z

Source-reported events for the cited work

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Observation 67f47fcc-6170-4058-8629-ab1a673e700b · outbound

This paper cites Large language models as tax attorneys: A case study in legal capabilities emergence,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Large language models as tax attorneys: A case study in legal capabilities emergence,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.178493Z

Source-reported events for the cited work

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

Observation 5b1f8f31-8fa4-4ab0-8e44-5044cc48d985 · outbound

This paper cites LegalMind system and the LLM-based legal judgment query system,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation LegalMind system and the LLM-based legal judgment query system,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.167854Z

Source-reported events for the cited work

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

Observation 74e953b7-ff5d-4d47-9c1c-858a97608e36 · outbound

This paper cites Can ChatGPT predict Chinese equity premiums?,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Can ChatGPT predict Chinese equity premiums?,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.157173Z

Source-reported events for the cited work

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

Observation 1ac19e18-2713-4456-bfee-57a4139e38f9 · outbound

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

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.135740Z

Source-reported events for the cited work

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

Observation 0a2bd441-8edc-425a-9870-6d0ed2ffdf26 · outbound

This paper cites ChatGPT: A canary in the coal mine or a parrot in the echo chamber? Detecting fraud with LLM: The case of FTX,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation ChatGPT: A canary in the coal mine or a parrot in the echo chamber? Detecting fraud with LLM: The case of FTX,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.146662Z

Source-reported events for the cited work

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

Observation a34ad9a3-2007-4ca4-a966-7bab14c5ad03 · outbound

This paper cites Language Models are Few-Shot Learners.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Language Models are Few-Shot Learners

Reference 9

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Source-reported events for the cited work

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Observation ce670b5b-5157-41d7-b862-dfd8bfe23ffc · outbound

This paper cites Anchoring revisited: Robust evidence from large-scale meta-analysis,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Anchoring revisited: Robust evidence from large-scale meta-analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.125337Z

Source-reported events for the cited work

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

Observation fbcf01de-4325-4f0f-b239-5b82a37e0da7 · outbound

This paper cites Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.115294Z

Source-reported events for the cited work

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

Observation 262671a3-601e-4fd3-b5ec-4ee1515f83b2 · outbound

This paper cites Natural frequencies illuminate base-rate neglect and facilitate Bayesian reasoning,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Natural frequencies illuminate base-rate neglect and facilitate Bayesian reasoning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.104476Z

Source-reported events for the cited work

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

Observation 9987f526-0393-410f-9aef-deef4b46fef9 · outbound

This paper cites Human behaviour in the context of low-probability, high- impact events,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Human behaviour in the context of low-probability, high- impact events,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.094219Z

Source-reported events for the cited work

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

Observation fa83d30d-15f1-41f0-ae88-fdc2ab87b0b2 · outbound

This paper cites Bad at probability? That might be a blessing,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Bad at probability? That might be a blessing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.083592Z

Source-reported events for the cited work

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

Observation 4b89b4df-9320-4145-b831-069770dcb070 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.071671Z

Source-reported events for the cited work

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

Observation 491d0bca-8e64-48d7-abb0-fee3973798e7 · outbound

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

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.060988Z

Source-reported events for the cited work

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

Observation 73e3bdac-ab6b-40af-b505-c8d23f89f56d · outbound

This paper cites Why should I trust you?: Explaining the predictions of any classifier,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Why should I trust you?: Explaining the predictions of any classifier,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.050201Z

Source-reported events for the cited work

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

Observation 37842fcc-5933-499d-913e-ecc2bf91a9fc · outbound

This paper cites Man is to computer programmer as woman is to homemaker? Debiasing word embeddings,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Man is to computer programmer as woman is to homemaker? Debiasing word embeddings,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.039068Z

Source-reported events for the cited work

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

Observation 33651c28-aa52-4aba-8efc-d1a9bc3022a2 · outbound

This paper cites The challenge of uncertainty quantification of large language models in medicine.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation The challenge of uncertainty quantification of large language models in medicine

Reference 19

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Source-reported events for the cited work

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Observation e0d1cc85-96cd-4081-82be-699b2ad34b03 · outbound

This paper cites Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.028502Z

Source-reported events for the cited work

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

Observation 39ae21ae-ad64-4d70-af1e-417965639f85 · outbound

This paper cites Uncertainty-Aware Explainable Recommendation with Large Language Models.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Uncertainty-Aware Explainable Recommendation with Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T22:45:42.558346Z

Source-reported events for the cited work

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Observation bdd83f3b-9c5e-4e04-b291-a6f1533d5911 · outbound

This paper cites A novel integration strategy for uncertain knowledge in group decision-making with artificial opinions: A DSFIT-SOA-DEMATEL approach,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation A novel integration strategy for uncertain knowledge in group decision-making with artificial opinions: A DSFIT-SOA-DEMATEL approach,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.018100Z

Source-reported events for the cited work

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

Observation 51f09a81-1fcb-419c-aa03-ff9c78c9ce2a · outbound

This paper cites Selective prediction-set models with coverage rate guarantees,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Selective prediction-set models with coverage rate guarantees,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:43.007362Z

Source-reported events for the cited work

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

Observation ae6cdcd4-17a0-4072-94c2-7b96355f6221 · outbound

This paper cites On the foundations of noise-free selective classification,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation On the foundations of noise-free selective classification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.997174Z

Source-reported events for the cited work

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

Observation 3327559e-3998-4701-9375-324d3cf58860 · outbound

This paper cites Holistic Evaluation of Language Models.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Holistic Evaluation of Language Models

Reference 25

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Source-reported events for the cited work

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Observation fc859ce6-f0ce-41c9-a359-f2c5f2daeb99 · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T22:45:42.576671Z

Source-reported events for the cited work

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Observation 3312b6a7-90ae-42ec-b004-e8803bf2dbc0 · outbound

This paper cites Dlugatch, A.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Dlugatch, A

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.986896Z

Source-reported events for the cited work

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Observation be9ebee6-5f2f-449a-b51f-d5abc4b7d05c · outbound

This paper cites Mechanisms of cancer metastasis,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Mechanisms of cancer metastasis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.976560Z

Source-reported events for the cited work

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

Observation a5a77578-179b-41ea-ba67-1bd1e6aa0ebd · outbound

This paper cites An exploratory survey about using ChatGPT in education, healthcare, and research,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation An exploratory survey about using ChatGPT in education, healthcare, and research,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.965895Z

Source-reported events for the cited work

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

Observation 7b754cb9-974d-49d9-9211-0df252461d52 · outbound

This paper cites Selectively answering ambiguous questions,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Selectively answering ambiguous questions,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.954842Z

Source-reported events for the cited work

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

Observation 4ef91974-31ce-468a-aeaf-5c4d3f6e12bd · outbound

This paper cites Selective question answering under domain shift,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Selective question answering under domain shift,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.944191Z

Source-reported events for the cited work

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Observation 1f255a54-b42f-432b-911f-d0224497aa47 · outbound

This paper cites Calibration of Pre-trained Transformers,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Calibration of Pre-trained Transformers,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.932485Z

Source-reported events for the cited work

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

Observation 73f4b65e-d3bd-4c3f-9e28-489f6daa3e50 · outbound

This paper cites COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T22:45:42.601650Z

Source-reported events for the cited work

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Observation 070f8649-929d-4ed3-9453-a580042e9b30 · outbound

This paper cites Logic-LM: Empow- ering large language models with symbolic solvers for faithful logical reasoning,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Logic-LM: Empow- ering large language models with symbolic solvers for faithful logical reasoning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.922167Z

Source-reported events for the cited work

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

Observation eff75d5d-a0bd-4989-b909-3d88da1a157a · outbound

This paper cites Deep sym- bolic regression for recurrent sequences,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Deep sym- bolic regression for recurrent sequences,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.910228Z

Source-reported events for the cited work

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

Observation 1569d65c-b0b2-45f7-8349-081c0714b203 · outbound

This paper cites A comprehensive survey on neuro-symbolic artificial intelligence,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation A comprehensive survey on neuro-symbolic artificial intelligence,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.898273Z

Source-reported events for the cited work

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

Observation 998f7354-e7ea-4322-8f7a-4517f7afa7da · outbound

This paper cites Integrating artificial intelligence with the Internet of Things: A review of recent advances,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Integrating artificial intelligence with the Internet of Things: A review of recent advances,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.888116Z

Source-reported events for the cited work

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

Observation f47e9b6a-d115-4488-a3c6-b88e55a2e110 · outbound

This paper cites Historical perspectives on the development of neuro-symbolic reasoning,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Historical perspectives on the development of neuro-symbolic reasoning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.877565Z

Source-reported events for the cited work

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

Observation e001fbc3-aade-4e52-9b84-af4c516e65e1 · outbound

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Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:45:42.866832Z

Source-reported events for the cited work

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

Observation 76078edd-d723-4fa0-a2b6-438ec80ec7dc · outbound

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Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:45:42.856460Z

Source-reported events for the cited work

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

Observation 93b28ec3-ed69-4343-9ee6-82ca604a6147 · outbound

This paper cites Axiomatizing Conditional Normative Reasoning.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Axiomatizing Conditional Normative Reasoning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.845570Z

Source-reported events for the cited work

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

Observation de0d8b39-9625-4b33-bd94-4a46da0e41e5 · outbound

This paper cites A Virtue-Based Framework to Support Putting AI Ethics into Practice,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation A Virtue-Based Framework to Support Putting AI Ethics into Practice,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.834639Z

Source-reported events for the cited work

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

Observation 922fd08a-87a1-4c49-b703-0688399cd4d3 · outbound

This paper cites an unresolved cited work.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:45:42.822535Z

Source-reported events for the cited work

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

Observation d12e722e-c42a-427b-8143-81e105097276 · outbound

This paper cites an unresolved cited work.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:45:42.811122Z

Source-reported events for the cited work

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

Observation 3ce21e5b-2dc2-4c92-b756-96330f501d76 · outbound

This paper cites an unresolved cited work.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:45:42.800399Z

Source-reported events for the cited work

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

Observation e7823842-feb6-4fee-9d84-7e390ed2d9aa · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Teaching Models to Express Their Uncertainty in Words

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T22:45:42.647928Z

Source-reported events for the cited work

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Observation 20044f8a-8d6f-48b2-825a-be17a73040b1 · outbound

This paper cites Greene, Moral tribes: Emotion, reason, and the gap between us and the.m New York, NY , USA: Penguin, 2013.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation Greene, Moral tribes: Emotion, reason, and the gap between us and the.m New York, NY , USA: Penguin, 2013

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.788842Z

Source-reported events for the cited work

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

Observation d9ee551f-a00a-427e-b9aa-4b21113387e6 · outbound

This paper cites The emotional dog and its rational tail: A social intuitionist approach to moral judgment,.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation The emotional dog and its rational tail: A social intuitionist approach to moral judgment,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:45:42.776406Z

Source-reported events for the cited work

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

Pith citing papers

No inbound Pith citation observations are available.