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Paper Citation Record · LEDGER

On Measures of Biases and Harms in NLP

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.

pith.paper-citation-record.v1
2108.03362 v2

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 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:33:42.398370Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:08:12.285693Z

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 1f0ca1b5-cf88-4484-aab7-b96e29271fca · inbound

BBQ: A Hand-Built Bias Benchmark for Question Answering cites this paper.

BBQ: A Hand-Built Bias Benchmark for Question Answering On Measures of Biases and Harms in NLP

Reference 54

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T19:55:11.535267Z

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.

source=arxiv_source observed=2026-05-16T19:55:11.431020Z digest=sha256:cc10e8ba66b7c833e841ab55dcc87713a3f9c1fdcb7d8b3ce030a50296c0c162

Observation 5485506b-67b7-4525-b591-834166177643 · inbound

PaLM: Scaling Language Modeling with Pathways cites this paper.

PaLM: Scaling Language Modeling with Pathways On Measures of Biases and Harms in NLP

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:45:07.180164Z

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.

source=arxiv_source observed=2026-05-10T23:45:06.755839Z digest=sha256:b695ea95666e13c6dc9dcfc254f7bbbd2687bbba9e7d63c9431fa894d84891ac

Observation 4e859c6b-fb0c-419c-9ce5-f1923676abcf · inbound

PaLM 2 Technical Report cites this paper.

PaLM 2 Technical Report On Measures of Biases and Harms in NLP

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-12T11:59:27.107248Z

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.

source=arxiv_source observed=2026-05-12T11:59:25.813128Z digest=sha256:9d871414520074d92060f6526fab97c16dd1703877375efc985f5f27fd7fff10

Observation ab9c289f-6825-41f3-af5b-e9685e477c23 · inbound

Bias in Large Language Models: Origin, Evaluation, and Mitigation cites this paper.

Bias in Large Language Models: Origin, Evaluation, and Mitigation On Measures of Biases and Harms in NLP

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:08:12.288835Z

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.

source=pdf_text observed=2026-05-23T17:08:09.267577Z digest=sha256:44ad211a6e534ca6bf0b090d0c10568db6a978166a855618d144dee640319992

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 cites this paper.

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

Resolution
unresolved
no resolver link, observed 2026-08-15T17:33:42.398370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:33:42.398370Z digest=sha256:2345b54a30361c9afbb7b29e337d3fbfdae261c148c90a1e5d1b406978b03045

Observation f50ae4ef-30ea-4f14-89a6-8b1f2bea8609 · inbound

Social Bias in LLM-Generated Code: Benchmark and Mitigation cites this paper.

Social Bias in LLM-Generated Code: Benchmark and Mitigation On Measures of Biases and Harms in NLP

Reference 143

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:36:08.855408Z

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.

source=arxiv_source observed=2026-05-09T19:34:51.433422Z digest=sha256:a79c5745cc6ff8e71882f834e8a820c5d28a5605dd455eb47ed155424ca20e52