Paper Citation Record · LEDGER

Evaluating the Robustness of Neural Language Models to Input Perturbations

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2108.12237.

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

pith.paper-citation-record.v1
2108.12237 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:12:04.498683Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T06:25:21.000290Z

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 e121a430-b6ba-4db3-a443-e334b19ab1c9 · inbound

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts cites this paper.

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Evaluating the Robustness of Neural Language Models to Input Perturbations

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:25:21.003224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76165553-3c36-4516-859b-9d6bf786ee50 · inbound

MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers cites this paper.

MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers Evaluating the Robustness of Neural Language Models to Input Perturbations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T01:05:23.133177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e55ae5a-b975-42ee-9ecc-f7a9a758d606 · inbound

Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning cites this paper.

Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning Evaluating the Robustness of Neural Language Models to Input Perturbations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:12:04.498683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ac6a081-2131-489e-aab5-3c81dd804c36 · inbound

Agent Identity Evals: Measuring Agentic Identity cites this paper.

Agent Identity Evals: Measuring Agentic Identity Evaluating the Robustness of Neural Language Models to Input Perturbations

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T14:57:06.893117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8653362-7194-47f1-bbe8-724f0738c7ae · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models Evaluating the Robustness of Neural Language Models to Input Perturbations

Reference 92

Resolution: unresolved; no resolver link, observed 2026-07-14T08:45:52.855783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.