Paper Citation Record · LEDGER

Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.12649.

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

pith.paper-citation-record.v1
2402.12649 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:21:55.764244Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T02:25:19.227782Z

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 5f8548e3-e40f-40a1-8c53-a072564e0b2d · inbound

HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter cites this paper.

HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T14:21:55.764244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7658f5f-b9a9-4386-803c-169039fbba94 · inbound

Towards Effective Discrimination Testing for Generative AI cites this paper.

Towards Effective Discrimination Testing for Generative AI Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:44.908977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9566273-dcf3-4b57-9649-4fa3175e583f · inbound

Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation cites this paper.

Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-08T15:06:55.061220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 785e9a45-d530-4cc0-a978-16f46fd0f709 · inbound

Thinking beyond the anthropomorphic paradigm benefits LLM research cites this paper.

Thinking beyond the anthropomorphic paradigm benefits LLM research Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T22:21:52.615776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f67434-d7c3-49cc-8ba5-3aa16d52beb8 · 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 Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:25:19.230443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e901355-9313-465b-ac3b-41310f954989 · inbound

Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities cites this paper.

Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:09.184748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.