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Paper Citation Record · LEDGER

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment

As of 8 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 0 inbound Pith citation observations for arXiv:2607.05552.

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

pith.paper-citation-record.v1
2607.05552 v1

Coverage vector

measured 100 of 117 reference resolution

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Reference resolution

100 of 117 outbound references displayed

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Outbound references

Observation db003efe-5b93-495f-b2f7-9a812aa86a18 · outbound

This paper cites The framing of decisions and the psychology of choice.Science, 211(4481):453–458, 1981.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The framing of decisions and the psychology of choice.Science, 211(4481):453–458, 1981

Reference 1

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This paper cites Choices, values, and frames.American Psychologist, 39(4):341–350, 1984.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Choices, values, and frames.American Psychologist, 39(4):341–350, 1984

Reference 2

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This paper cites Levin, Sandra L.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Levin, Sandra L

Reference 3

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This paper cites A systematic review of risky-choice framing effects.EXCLI Journal, 22:1012–1031, 2023.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment A systematic review of risky-choice framing effects.EXCLI Journal, 22:1012–1031, 2023

Reference 4

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This paper cites Influence of wording and framing effects on moral intuitions.Ethology and Sociobiology, 17(3):145–171, 1996.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Influence of wording and framing effects on moral intuitions.Ethology and Sociobiology, 17(3):145–171, 1996

Reference 5

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This paper cites Order effects in moral judgment.Philosophical Psychology, 25(6):813–836,.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Order effects in moral judgment.Philosophical Psychology, 25(6):813–836,

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 7

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This paper cites Expertise in moral reasoning? order effects on moral judgment in professional philosophers and non-philosophers.Mind & Language, 27(2): 135–153, 2012.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Expertise in moral reasoning? order effects on moral judgment in professional philosophers and non-philosophers.Mind & Language, 27(2): 135–153, 2012

Reference 8

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This paper cites Philosophers’ biased judgments persist despite training, expertise and reflection.Cog- nition, 141:127–137, 2015.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Philosophers’ biased judgments persist despite training, expertise and reflection.Cog- nition, 141:127–137, 2015

Reference 9

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This paper cites Large lan- guage models show amplified cognitive biases in moral decision- making.Proc.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Large lan- guage models show amplified cognitive biases in moral decision- making.Proc

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

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This paper cites Robustness of large language models in moral judgements.Royal Society Open Science, 12 (4):241229, 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Robustness of large language models in moral judgements.Royal Society Open Science, 12 (4):241229, 2025

Reference 12

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This paper cites The greatest good benchmark: Measuring LLMs’ alignment with utilitarian moral dilemmas.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The greatest good benchmark: Measuring LLMs’ alignment with utilitarian moral dilemmas

Reference 13

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This paper cites Murukannaiah, and Munindar P.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Murukannaiah, and Munindar P

Reference 14

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Varshney

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting

Reference 17

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Large language models sensitivity to the order of options in multiple-choice questions

Reference 18

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment doi: 10.18653/ v1/2024.findings-naacl.130

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Large language models are not robust multiple choice selectors

Reference 20

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Questioning the survey responses of large language models

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Do LLMs exhibit human-like response biases? a case study in survey design.Transactions of the Association for Computational Linguistics, 12:1011–1026, 2024

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Political compass or spinning arrow? towards more meaningful evaluations for values and opinions in large language models

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This paper cites State of what art? a call for multi-prompt LLM evaluation.Transactions of the Association for Computational Linguistics, 12:933–949, 2024.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment State of what art? a call for multi-prompt LLM evaluation.Transactions of the Association for Computational Linguistics, 12:933–949, 2024

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Promptrobust: Towards evaluating the 12 robustness of large language models on adversarial prompts

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment In-context impersonation reveals large lan- guage models’ strengths and biases

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Aligning AI with shared human values

Reference 27

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Xing, Hao Zhang, Joseph E

Reference 29

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This paper cites Whose opinions do language models reflect? InProceedings of the 40th International Conference on Machine Learning, volume 202 ofPMLR, pages 29971–30004, 2023.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Whose opinions do language models reflect? InProceedings of the 40th International Conference on Machine Learning, volume 202 ofPMLR, pages 29971–30004, 2023

Reference 30

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The effect of sampling temper- ature on problem solving in large language models

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The good, the bad, and the greedy: Evaluation of LLMs should not ignore non-determinism

Reference 32

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Hashimoto, and Tobias Gerstenberg

Reference 33

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Moral foundations of large language models

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

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Observation 4df1abe9-47bc-4a1c-bbdd-c33b21e8db52 · outbound

This paper cites The moral machine experiment on large language models.Royal Society Open Science, 11(2):231393,.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The moral machine experiment on large language models.Royal Society Open Science, 11(2):231393,

Reference 37

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Observation f9d9cfd3-3c33-47fb-bbdc-78ae0bd546ad · outbound

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 38

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Observation 94604a39-4fae-4021-8bc1-8fb35215ac61 · outbound

This paper cites When to make exceptions: Exploring language models as accounts of human moral judg- ment.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment When to make exceptions: Exploring language models as accounts of human moral judg- ment

Reference 39

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Observation e7b7e4b5-5da0-4648-a8ca-0e8f7caf96e5 · outbound

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The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 40

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Observation 467ab33f-581a-466e-a00d-30d3b9810741 · outbound

This paper cites MoralBench: Moral evaluation of LLMs, 2024.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment MoralBench: Moral evaluation of LLMs, 2024

Reference 41

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Observation a3504c81-be2c-428b-bf21-53e531048a49 · outbound

This paper cites Campbell and Donald W.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Campbell and Donald W

Reference 42

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Observation 2e1e2646-0ea4-4d49-8bc8-885d53f705d2 · outbound

This paper cites Cronbach and Paul E.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Cronbach and Paul E

Reference 43

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Observation 88bcfbd8-1872-448b-965e-1966cd345b0c · outbound

This paper cites Academic Press, New York, 1981.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Academic Press, New York, 1981

Reference 44

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Observation 6cf163ff-d4ff-4988-b267-f74be24711ae · outbound

This paper cites Billiet and McKee J.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Billiet and McKee J

Reference 45

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Observation b0751a11-a81e-4f85-8b19-19a539061c06 · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 46

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:344c287b1e6b12328d12aa1b008be39462dfceca0aeaabc6f248b3b27ef8ae8f

Observation 4c5d5798-7bc4-4a44-8ef2-6d2e75b5802d · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 47

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:de40ba085450661dbc75e7d7a8f72477de123b138b97fad447696802280fc3e0

Observation deedc705-759d-4d85-8429-a7a020e7a730 · outbound

This paper cites Krosnick and Duane F.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Krosnick and Duane F

Reference 48

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:6f9ce8c44e7626f4cdc47d4abdbb07ae71a313cbb83d597a0061883099b8be27

Observation 1692d94e-0432-4c9e-a4d8-59ca5d35cf89 · outbound

This paper cites Krosnick.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Krosnick

Reference 49

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Observation 43eccd2d-afbd-4e39-bcdc-a0431f82e283 · outbound

This paper cites Nelder.Generalized Linear Models.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Nelder.Generalized Linear Models

Reference 50

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Observation 8472f8f3-8899-4897-b012-e6a7603b84ee · outbound

This paper cites Danish Institute for Educational Research, Copenhagen, 1960.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Danish Institute for Educational Research, Copenhagen, 1960

Reference 51

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:7424babedc3bcce4e1fea3b24cbbea1b6e6507940d2748252e3b08f43adff154

Observation c732df1a-f998-42d7-a816-301045e4a991 · outbound

This paper cites Some latent trait models and their use in inferring an examinee’s ability.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Some latent trait models and their use in inferring an examinee’s ability

Reference 52

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Observation 2ebdc6ca-7d3b-4b5e-9b73-fb8ba35d073a · outbound

This paper cites Lord.Applications of Item Response Theory to Prac- tical Testing Problems.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Lord.Applications of Item Response Theory to Prac- tical Testing Problems

Reference 53

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:fbfb1f3ae5af6c6928e65a332da291244212e74e5359513e1af0acf2be50bdc1

Observation e50144b3-0777-4c06-8b93-08faf91a23f2 · outbound

This paper cites Embretson and Steven P.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Embretson and Steven P

Reference 54

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:7e55b86c3e5e0a0b6d6ea5eea141d3b69e7b5ed5ef2b22662b716c49249b383d

Observation 2ab6d91b-567a-427c-83f5-e3504b9a0742 · outbound

This paper cites large language models show amplified cogni- tive biases in moral decision-making.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment large language models show amplified cogni- tive biases in moral decision-making

Reference 55

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:f85f2af8bbc8b5b41194e0344959034e5a1174f1e7bdeb5c0309caa6f8add792

Observation b8ecc2a4-f647-4f37-94b0-d41981f26a60 · outbound

This paper cites Rothkopf, and Kristian Kersting.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Rothkopf, and Kristian Kersting

Reference 56

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:199743761b2ab59888bbed6ef8d64a0a3185239e8b2f68156cbffcf97cc38ca6

Observation 632272e6-23a6-4f03-8438-fda4f7d559f0 · outbound

This paper cites Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity

Reference 57

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:f4e9c635fb65666aa301700144f041fc9f5eb8899ff18665f21b29d77e827cd1

Observation 95cd3423-933c-47bb-9ec6-c3ce2f05d9cd · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 58

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:d1da217f8cb5227059a808a32bf6d657a3e5d16fedc8acaf02e04acd105a3c35

Observation ca759ed5-3f76-48bf-89cc-f0f67c3c07e6 · outbound

This paper cites Language model alignment in multilingual trolley problems.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Language model alignment in multilingual trolley problems

Reference 59

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:cfb41f82395b7d42f56d28962f70c38b6ec8835332c8e9a34ad131a047e17082

Observation 9760a7f9-2a06-477d-9f72-135a0274b0d4 · outbound

This paper cites Who is GPT-3? An exploration of personality, values and demographics.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Who is GPT-3? An exploration of personality, values and demographics

Reference 60

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:7ded1b6a7550c0c286ad5b6f5f6e39244781fe5ed6ad8bd7263049c7c3718ab2

Observation 1c83eac3-7015-4de9-9424-49a64c50b4ed · outbound

This paper cites The self-perception and po- litical biases of ChatGPT.Human Behavior and Emerging Technologies, 2024:1–9, 2024.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The self-perception and po- litical biases of ChatGPT.Human Behavior and Emerging Technologies, 2024:1–9, 2024

Reference 61

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:1aefdc9b4ad97fc7a9395b87cb0bbd0b52a6081d9d1fdda5e795aee98c4437ca

Observation d9815f99-743b-41ca-ac51-a0ddc3877663 · outbound

This paper cites CMoralEval: A moral evaluation benchmark for Chinese large language mod- els.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment CMoralEval: A moral evaluation benchmark for Chinese large language mod- els

Reference 62

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:2073f363eb2e263de1913c02671d2fabfe79ca400adb69cb8de3cdbba29933d6

Observation 31002f31-a1f0-4f43-aebe-f1949d73b862 · outbound

This paper cites Llm ethics benchmark: A three-dimensional assessment system for evaluating moral reasoning in large language models.Scientific Reports, 15:34642,.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Llm ethics benchmark: A three-dimensional assessment system for evaluating moral reasoning in large language models.Scientific Reports, 15:34642,

Reference 63

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:59124265af23e97559c341ef1a6f2f8a19b00ecc8d232d012b29470df077916c

Observation 51ce4003-83dd-4934-af91-f3bf2552f487 · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 64

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:7e913eb1f22592bc8835e678a1c9841150ff53e994156fb721617ed0e4f93cd3

Observation 04185fac-257c-4764-a296-2af11df7ff0d · outbound

This paper cites Evaluating moral beliefs across LLMs through a pluralistic framework.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Evaluating moral beliefs across LLMs through a pluralistic framework

Reference 65

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:5f865144e6f095eb041e422bc7d1418f8576f70d7b0d9b512229f04c00a802df

Observation 08487d3c-65ab-4788-98f1-b3dd1d5ccc16 · outbound

This paper cites Cultural value alignment in large language mod- els: A prompt-based analysis of Schwartz values in Gemini, ChatGPT, and DeepSeek, 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Cultural value alignment in large language mod- els: A prompt-based analysis of Schwartz values in Gemini, ChatGPT, and DeepSeek, 2025

Reference 66

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:b7f73ac4097ade12aca9f9b5a4ec46a73aed20aff9258058dfde6404bf9484c1

Observation 909d0e3a-4c39-461f-9f70-f5c3f47e761c · outbound

This paper cites Large language models are not fair evaluators.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Large language models are not fair evaluators

Reference 67

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:ccd86a4b0c5ffff4ee7933676645630e580f89bfcc1bf9ce741adda366705fe0

Observation 03010676-e8a3-4135-a994-6bf0b29f7c13 · outbound

This paper cites Discovering language model behaviors with model-written evaluations.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Discovering language model behaviors with model-written evaluations

Reference 68

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:6e32b03bf095ad17f03b7c283356671decc59744fa783848ffde1f869a84829c

Observation c0496e75-fee4-46df-8ded-db2c0c45c609 · outbound

This paper cites Bowman, Newton Cheng, Esin Dur- mus, Zac Hatfield-Dodds, Scott R.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Bowman, Newton Cheng, Esin Dur- mus, Zac Hatfield-Dodds, Scott R

Reference 69

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:33d6957eb7b977842484ca3554ac08c792225c27773744f3ba72695cd652d60d

Observation ed604c76-1351-4fcb-a49f-bdf05faa00b5 · outbound

This paper cites Primacy effect of ChatGPT.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Primacy effect of ChatGPT

Reference 70

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:1e5e5bc80a00e6d022a27c6a7ca29f2a95a6c1078ef35b62fe625f7035c1b861

Observation eeb53616-f3d4-470e-bf51-5eea1023445d · outbound

This paper cites Prompt perturbations reveal human-like biases in large language model survey responses.arXiv preprint arXiv:2507.07188, 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Prompt perturbations reveal human-like biases in large language model survey responses.arXiv preprint arXiv:2507.07188, 2025

Reference 71

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:d75d5e585b59fd414c02a755dca8eb578bd723ebdd9912bbaf4e2caa2476105e

Observation 8098eb54-0e9e-4390-b1a9-e5253f517812 · outbound

This paper cites Acquiescence bias in large language models.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Acquiescence bias in large language models

Reference 72

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:4382b1edb8462eb5bd33461533295a0a8540fe07d382e9127ffac83d55643138

Observation b1a77a85-e5ee-45ae-bc4a-1bdeebc66494 · outbound

This paper cites Machine Psychology.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Machine Psychology

Reference 73

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:3bf43acde64988be62513faf4ad7064a5fb25d470ef8b856338023f27cb5b928

Observation 6a272599-aa96-4a48-b2c7-be5bf5d99a66 · outbound

This paper cites Using cognitive psychology to understand GPT-3.Proceedings of the National Academy of Sci- ences, 120(6):e2218523120, 2023.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Using cognitive psychology to understand GPT-3.Proceedings of the National Academy of Sci- ences, 120(6):e2218523120, 2023

Reference 74

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:613a3a8adb722a31b760a1067e0e47f0b48d1cfd6bc6d6cde835f9ee73de386e

Observation 92e1640f-3ee6-4ba0-b10c-791fb725852d · outbound

This paper cites Wang, and Eric Schulz.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Wang, and Eric Schulz

Reference 75

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:2dc13e77a32dc4dbff1718413b1118a512ef69a2200e2b900ed39702902553e8

Observation 1ba03363-a39d-42c3-b7da-673c194249be · outbound

This paper cites Argyle, Ethan C.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Argyle, Ethan C

Reference 76

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:d67f041b7d9c1869fbe6019461f7c1cca8793058ddb487a61d4a94b77c6970e7

Observation d5df5fb7-a630-4777-ba15-91fcc6b87043 · outbound

This paper cites Arriaga, and Adam Tauman Kalai.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Arriaga, and Adam Tauman Kalai

Reference 77

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:382811a7fd125121ad1c91308674051182d8a3473cacab54ca411bf27df61857

Observation 7ef38444-d690-4ccc-acd1-f992639de8eb · outbound

This paper cites Yeager, Christopher J.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Yeager, Christopher J

Reference 78

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:4b6f0ba75bd007ecebc0634d3399f08b70d054a339a077d54129d1d2ec724179

Observation 90ad987f-b710-4dcf-b42b-e60c6973bbff · outbound

This paper cites Talking about large language models.Com- munications of the ACM, 67(2):68–79, 2024.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Talking about large language models.Com- munications of the ACM, 67(2):68–79, 2024

Reference 79

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:1ef3e3d506266e105bf63becd3d0d77acd3c5b97f9c9ae302c9f18706a225105

Observation 36629b23-f3f2-48cc-a900-715cf1b267d3 · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 80

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:c11ddee288431d2efb0491c3f2599d68d99cafe127378a8b89cb05106e4c7442

Observation f3605b2f-e007-41ba-a5ef-7021b3808e29 · outbound

This paper cites Can AI language models replace human participants?Trends in Cognitive Sciences, 27(7):597–600, 2023.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Can AI language models replace human participants?Trends in Cognitive Sciences, 27(7):597–600, 2023

Reference 81

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:709d8cc6adedeb0bc377ceb3e5091f9e99e0d3727d80e483e187d41bd49fbc48

Observation 3212cad4-475f-4eba-a4c1-bd8db321c2bb · outbound

This paper cites AI language model rivals expert ethicist in perceived moral expertise.Scientific Reports, 15:4084, 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment AI language model rivals expert ethicist in perceived moral expertise.Scientific Reports, 15:4084, 2025

Reference 82

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:843885e937e4f79b4c7b7ff66e736ae9bf31351bb607046440a0c0806d0a3192

Observation b2bb8b5c-7089-425a-9ba3-b914717c8fcc · outbound

This paper cites Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, and Victor Crespo.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, and Victor Crespo

Reference 83

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:b61d53793d1f8b5636af1be1cadd3efa76238a29df681e8288cbecc0a7c5ee02

Observation d4ee74b6-a6db-48a6-a575-1abb21d475fa · outbound

This paper cites The moral machine experiment.Nature, 563(7729): 59–64, 2018.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment The moral machine experiment.Nature, 563(7729): 59–64, 2018

Reference 84

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:17a7e8e0887e299eb3fe65a62d68710b1ef39a137e6e5ec0ff3675fbe50ce326

Observation 7599aea0-ce7e-4676-afa8-6d3dad50340b · outbound

This paper cites Lechner, Claudia Wagner, Beatrice Rammstedt, and Markus Strohmaier.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Lechner, Claudia Wagner, Beatrice Rammstedt, and Markus Strohmaier

Reference 85

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:da83d8945bcdcb06937674553415a1418c130740ecd55354d3088906abdde404

Observation 1efdff12-1f22-4b16-bf32-ec38fd07c4a4 · outbound

This paper cites Ullman, Fernando Martinez-Plumed, Joshua B.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Ullman, Fernando Martinez-Plumed, Joshua B

Reference 86

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:12b706c18f827c3bc27d74c0344efb79d62f72dc611c5ab738982e1a133fb935

Observation 525d35d2-f247-4b05-be4c-4dea5df6ebea · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 87

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:5c18855b0aaf7ee0975021ade416ce578440d80d8dcf1ae864ef3aa3842fb14a

Observation 04fb7e54-5d6c-4c50-8056-1bec4cbe2a0a · outbound

This paper cites Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna

Reference 88

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:a2557d0fa9b228876104b403ad287a2eb98cb9df15d5140afa77d01f3f245e04

Observation 904f5280-94a5-4498-a9a4-8216c9ef1448 · outbound

This paper cites Do large language model benchmarks test reliability?, 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Do large language model benchmarks test reliability?, 2025

Reference 89

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:2e642f0c965eaddf50f002940e0b6f8a565687b84b31a9c0f5d062fcb4143acd

Observation 33b72cad-2c19-4ce3-a695-1920ae6d36e0 · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 90

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:1ef6d49a630c75af34e09bfc95cbf89cf232513c507f4e85b874514916be557d

Observation 01093c1b-88e5-4974-ad58-148ef1fbaa28 · outbound

This paper cites Lost in bench- marks? rethinking large language model benchmarking with item response theory.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Lost in bench- marks? rethinking large language model benchmarking with item response theory

Reference 91

Resolution
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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:1334da7f33de68fb786e5b68237249809daeb4b6c07777d23f91c711f26ed440

Observation 6260b942-ef85-42bc-ab7c-5cc738cf3b06 · outbound

This paper cites Establishing construct validity in LLM capa- bility benchmarks requires nomological networks, 2026.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Establishing construct validity in LLM capa- bility benchmarks requires nomological networks, 2026

Reference 92

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:3f381b4ae765e55aa597c30e215440a04326321d55fb5259640f122435c1585a

Observation 5d4855e0-e18d-46e3-a499-31ae8408d1a5 · outbound

This paper cites Six fallacies in substituting large language models for human participants.Advances in Methods and Practices in Psychological Science, 8(3), 2025.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Six fallacies in substituting large language models for human participants.Advances in Methods and Practices in Psychological Science, 8(3), 2025

Reference 93

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no resolver link, observed 2026-07-11T05:55:09.444392Z

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:2e8786abd2719fafc2654a5050b3f8f7746ef81f679759a997f742c1b6020e95

Observation ac7c7ea5-438e-44de-945d-ed48dbfc5241 · outbound

This paper cites Being blind (or not) to scenarios used in sacrificial dilemmas: the influence of factual and contextual information on moral responses.Frontiers in Psychology, 15:1477825, 2024.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Being blind (or not) to scenarios used in sacrificial dilemmas: the influence of factual and contextual information on moral responses.Frontiers in Psychology, 15:1477825, 2024

Reference 94

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verified exact
arxiv_id, observed 2026-07-11T05:57:50.625536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:0be6253dceb65e6952f2fd54deca70c550ae97b2b51a7969652833739692acec

Observation 84907128-d670-43aa-bb93-567e81e6e586 · outbound

This paper cites Cohen and Philip T.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Cohen and Philip T

Reference 95

Resolution
verified exact
doi, observed 2026-07-11T05:57:50.848679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:597819139e57e2cbb0771124c2510b454746905deb1aafa01ec5a8a1ae9eb9aa

Observation 05d18de9-d304-4ec3-b088-5bf18abefd41 · outbound

This paper cites Christensen and Antoni Gomila.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Christensen and Antoni Gomila

Reference 96

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source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:6eaa6c100a93017d51bfd8c3493c6782c1e4fc72663ca02126f4ac89df74acd4

Observation f01e3ca9-f849-4248-a587-cbb3ff768ed6 · outbound

This paper cites an unresolved cited work.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Unresolved cited work

Reference 97

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doi, observed 2026-07-11T05:57:50.897713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:753d48c12fc2531b6af600c76e165287a9adab4c9842be3d36cbaa0a0fab80a7

Observation acee78f4-2177-41e4-87d0-c3da4c7335b2 · outbound

This paper cites How stable are moral judgments?Review of Philosophy and Psychology, 14(4): 1377–1403, 2023.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment How stable are moral judgments?Review of Philosophy and Psychology, 14(4): 1377–1403, 2023

Reference 98

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:85b7d14c84c8db2d294cca14f08d38938c94bd65faa825078fa2374125c7d5ac

Observation cf0f409d-f4ad-4926-8724-80851925f26b · outbound

This paper cites Discrepancies between judgment and choice of action in moral dilemmas.Frontiers in Psychology, 4:250, 2013.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment Discrepancies between judgment and choice of action in moral dilemmas.Frontiers in Psychology, 4:250, 2013

Reference 99

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verified exact
arxiv_id, observed 2026-07-11T05:57:50.962832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:91b4ffc814838fdf87e1503dd2cf8d1e789649eeed4fd57f0eeab2a13b2a6c62

Observation 825bca55-fa47-4372-87a9-4cd6c3ba6cb0 · outbound

This paper cites What we say and what we do: The relationship between real and hypothetical moral choices.

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment What we say and what we do: The relationship between real and hypothetical moral choices

Reference 100

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-11T05:55:09.444392Z digest=sha256:eebd36e1fd82b66869273a64fb7f70f607eaeb2526dfb1715125ec5d2c3ce566

Pith citing papers

No inbound Pith citation observations are available.