Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2404.16019.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T20:14:39.945078Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation b4a0023e-a508-40c5-b49d-c05d710cf016 · inbound
From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors cites this paper.
From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 85
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:55:25.067855Z
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.
Observation e3d06328-0019-4572-8a05-45803d729ac4 · inbound
A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies cites this paper.
A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T20:14:39.945078Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f4c2fa2f-329d-45e7-bf76-936d4a31be32 · inbound
Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights cites this paper.
Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T02:25:19.250326Z
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.
Observation 099db2a0-2c7e-4e0e-9b9a-d8c074b379ca · inbound
Reinforcement Learning from Human Feedback cites this paper.
Reinforcement Learning from Human Feedback The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 219
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-22T19:32:01.116123Z
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.
Observation 5c788646-bf08-401b-9ac3-3e9faabe01c5 · inbound
Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas cites this paper.
Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:36:25.575004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f020e35e-8e2b-459d-92af-e2a615d067e2 · inbound
Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries cites this paper.
Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:59:54.208609Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99389597-b6f3-47e8-b76d-019026ebe151 · inbound
ModelCitizens: Representing Community Voices in Online Safety cites this paper.
ModelCitizens: Representing Community Voices in Online Safety The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:31:01.028060Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d532aa9-d4a7-4f19-8b3e-9fd5fbdacc58 · inbound
CALMA: A Process for Deriving Context-aligned Axes for Language Model Alignment cites this paper.
CALMA: A Process for Deriving Context-aligned Axes for Language Model Alignment The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:10:17.396119Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fbfda5d2-2bfb-4904-92fe-67ec525ec613 · inbound
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models cites this paper.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:08:15.657089Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0b126402-104c-43ad-8713-555261521a8b · inbound
Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge? cites this paper.
Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge? The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:02:21.712413Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ba02fb1-7269-4f2b-b4cb-be14cd2067d5 · inbound
From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models cites this paper.
From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming 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 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:45:20.458985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b13b63a9-5e87-4d59-bc76-19469dc249c5 · inbound
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 95
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:13:06.279576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07b5b54a-1e3c-40a4-961c-e0fe3acb6855 · inbound
Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation cites this paper.
Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:15:49.605724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdca57e2-9886-4974-b0ac-0aabbbc8c58d · inbound
CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis cites this paper.
CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:31:39.405166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3367a97a-95c9-4f73-852c-b85ca06249d4 · inbound
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs cites this paper.
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T10:26:11.001548Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f9bf42b-a81d-412e-a93d-328164bbef2e · inbound
The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs cites this paper.
The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T09:51:28.102333Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18a62e9e-8305-42d0-8304-df10ee1a8434 · inbound
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters cites this paper.
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T11:56:44.282967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b53c8e36-d1af-417e-9e1b-2dda439bb0cc · inbound
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments cites this paper.
PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T09:59:59.010071Z
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.
Observation 2eb926fe-2a56-45cc-ac50-2315271bd8d3 · inbound
Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics cites this paper.
Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 67
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T21:03:20.000053Z
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.
Observation 36e46557-cd9d-450d-bc40-d69794363e5d · inbound
Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics cites this paper.
Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 67
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T10:40:00.724625Z
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.
Observation add667fc-e0b1-44f0-a11f-cbe39d9322d7 · inbound
Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem cites this paper.
Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T23:04:17.656034Z
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.
Observation e1b02476-f799-4615-8af3-fa978ff9dbf4 · inbound
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement cites this paper.
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:36:25.395865Z
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.
Observation 332e7daf-7ad0-413c-b15d-397c3e3816bb · inbound
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement cites this paper.
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T22:23:47.253785Z
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.
Observation 2d5b941f-2b85-4630-ae31-8eeb50559b1e · inbound
ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions cites this paper.
ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T15:34:48.508266Z
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.
Observation 2c2cbd06-1c90-4e79-8b70-47d6976dc8c0 · inbound
What Do People Actually Want From AI? Mapping Preference Plurality cites this paper.
What Do People Actually Want From AI? Mapping Preference Plurality The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 50
- Resolution
- malformed identifier
- arxiv_id, observed 2026-07-02T13:06:59.559049Z
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.
Observation 2ee72ed4-fe4b-4c75-a215-e1feaeee4926 · inbound
PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration cites this paper.
PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T18:55:58.586609Z
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.
Observation ca464f72-47e4-4230-8f54-f92a19d82eee · inbound
Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF cites this paper.
Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T18:55:58.771241Z
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.
Observation 6d313d13-874b-4b2c-aeb8-3e47c5199c80 · inbound
Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins cites this paper.
Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:44:05.862197Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.