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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 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 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:10:13.322990Z

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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.

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 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.