Paper Citation Record · LEDGER

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2303.05453.

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

pith.paper-citation-record.v1
2303.05453 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:18:06.788813Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:49:37.702626Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 19e7cbeb-8c08-4828-bcf4-2a2631752187 · inbound

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction cites this paper.

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:49:13.926817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cfa11b76-1fae-4336-80fe-969def66a9d0 · inbound

Simple synthetic data reduces sycophancy in large language models cites this paper.

Simple synthetic data reduces sycophancy in large language models Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T14:48:08.591389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 01cd384b-c4b3-4a78-8d89-41595dbc1177 · inbound

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research cites this paper.

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-23T16:38:11.305159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9250377d-a000-4f0a-be72-6f57d9d72bbe · inbound

Unanswerability Evaluation for Retrieval Augmented Generation cites this paper.

Unanswerability Evaluation for Retrieval Augmented Generation Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T14:17:45.551643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f11e8e8c-618e-4904-881d-7dc866b1e9cb · inbound

HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation cites this paper.

HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:18:06.788813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c131207a-af7c-4569-8aaf-cec6c37972fc · inbound

Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review cites this paper.

Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:31:09.714470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeb88589-8f22-4f9b-87cf-030f99ab8e43 · inbound

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs cites this paper.

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T23:03:52.017534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a44011fa-b477-4436-a416-c0e60205bc3d · inbound

Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach cites this paper.

Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T21:30:36.406547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad360967-1a8c-4ff3-9fb6-f405d9ef5e9f · inbound

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions cites this paper.

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:13.322990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14c9880d-6b35-4781-ba18-26cdf04987b8 · inbound

Pairwise Calibrated Rewards for Pluralistic Alignment cites this paper.

Pairwise Calibrated Rewards for Pluralistic Alignment Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:48:01.748505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1debad8-dbc0-48e8-9be9-db9742ba00c8 · 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 Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T09:51:28.019805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca67274a-9916-4b5b-a3aa-0ad3255840c1 · inbound

"Label from Somewhere": Reflexive Annotating for Situated AI Alignment cites this paper.

"Label from Somewhere": Reflexive Annotating for Situated AI Alignment Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:07:48.390501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7a98f88-f135-4688-be63-afc18c412834 · inbound

When to Ask a Question: Understanding Communication Strategies in Generative AI Tools cites this paper.

When to Ask a Question: Understanding Communication Strategies in Generative AI Tools Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:17:02.509102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b02848c-5e46-4bcc-8644-b1b192369b69 · inbound

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences cites this paper.

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:32:35.343621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d664c1f8-2565-4da8-8e09-925a73b9b4f7 · inbound

AI Alignment From Social Choice Perspectives cites this paper.

AI Alignment From Social Choice Perspectives Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 124

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:49:37.704472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b1b79ee-17c3-4ba5-9dd3-2b5f723fffd7 · inbound

Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory cites this paper.

Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:09.813887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00a91046-ce5b-4259-9251-58c85057b1f6 · inbound

Procedural Fairness Failures in RLHF from Preference Averaging cites this paper.

Procedural Fairness Failures in RLHF from Preference Averaging Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T04:15:38.248174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.