Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:08:21.194755Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2507.13383.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:08:21.194755Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:27:05.818060Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T07:08:07.016432Z
62 of 62 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 818770d8-7653-49af-a56d-5e72856bbbed · outbound
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models URL app.prolific.com
Reference 1
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Aroyo and C
Reference 2
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models The Reasonable Effectiveness of Diverse Evaluation Data
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Aroyo, A
Reference 4
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Constitutional AI: Harmlessness from AI Feedback
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Bianchi, P
Reference 7
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models PERSONA: A Reproducible Testbed for Pluralistic Alignment
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 9
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Davani, M
Reference 10
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 13
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models A Survey on LLM-as-a-Judge
Reference 14
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Helff, F
Reference 15
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 16
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Kapania, A
Reference 17
Source-reported events for the cited work
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Kholodna, S
Reference 18
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Dynabench: Rethinking Benchmarking in NLP
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Reference 20
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 21
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Krippendorff
Reference 22
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 23
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Kumar, P
Reference 24
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 25
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 26
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Observation b68b7bbd-cd51-4e5f-9f9a-d04ddc24e829 · outbound
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Mostafazadeh Davani, M
Reference 27
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions
Reference 28
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Palomaki, O
Reference 29
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Generative Agents: Interactive Simulacra of Human Behavior
Reference 30
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Observation c35c5f26-f8bd-4656-a6e1-5dccff15ed86 · outbound
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
Reference 31
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Parrish, V
Reference 32
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Pavlick and T
Reference 33
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Pei and D
Reference 34
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Prabhakaran, A
Reference 35
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Prabhakaran, C
Reference 36
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Quaye, A
Reference 37
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Concrete Problems in AI Safety, Revisited
Reference 38
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 39
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Rismani, R
Reference 40
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Santurkar, E
Reference 41
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
Reference 42
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Schramowski, M
Reference 43
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Reference 44
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Sorensen, L
Reference 45
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Sorensen, J
Reference 46
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Sorensen, P
Reference 47
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Steiger, T
Reference 48
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Gemma: Open Models Based on Gemini Research and Technology
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models what is safety?
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Ethical and social risks of harm from Language Models
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models ShieldGemma 2: Robust and Tractable Image Content Moderation
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Unresolved cited work
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Not harmful
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models trains hitting a car in a violent accident
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models ISBN 978-1-939133-25-0
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
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