Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2108.03362.
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
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:33:42.398370Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T17:08:12.285693Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 1f0ca1b5-cf88-4484-aab7-b96e29271fca · inbound
BBQ: A Hand-Built Bias Benchmark for Question Answering On Measures of Biases and Harms in NLP
Reference 54
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5485506b-67b7-4525-b591-834166177643 · inbound
PaLM: Scaling Language Modeling with Pathways On Measures of Biases and Harms in NLP
Reference 35
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4e859c6b-fb0c-419c-9ce5-f1923676abcf · inbound
PaLM 2 Technical Report On Measures of Biases and Harms in NLP
Reference 36
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ab9c289f-6825-41f3-af5b-e9685e477c23 · inbound
Bias in Large Language Models: Origin, Evaluation, and Mitigation On Measures of Biases and Harms in NLP
Reference 20
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e318d2c5-9b43-42c0-b906-f1ab613b604c · inbound
Bias is a Math Problem, AI Bias is a Technical Problem: 10-year Literature Review of AI/LLM Bias Research Reveals Narrow [Gender-Centric] Conceptions of 'Bias', and Academia-Industry Gap On Measures of Biases and Harms in NLP
Reference 28
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f50ae4ef-30ea-4f14-89a6-8b1f2bea8609 · inbound
Social Bias in LLM-Generated Code: Benchmark and Mitigation On Measures of Biases and Harms in NLP
Reference 143
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.