Paper Citation Record · LEDGER

On Adversarial Robustness of Language Models in Transfer Learning

As of 14 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2501.00066.

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

pith.paper-citation-record.v1
2501.00066 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:41.821380Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T13:41:37.102367Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T13:42:19.227832Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy6
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e386a854-f6a6-40c6-9f39-81eb710c1260 · outbound

This paper cites Towards assurance of llm adversarial robustness using ontology-driven argumentation.

On Adversarial Robustness of Language Models in Transfer Learning Towards assurance of llm adversarial robustness using ontology-driven argumentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.214021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation edfbf781-8319-43de-a00d-513a6b7fd106 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

On Adversarial Robustness of Language Models in Transfer Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.746371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64400673-501a-4320-bfe8-e4046daa9680 · outbound

This paper cites Shortcut learning of large language models in natural language understanding.Communications of the ACM, 67(1): 110–120, 2023.

On Adversarial Robustness of Language Models in Transfer Learning Shortcut learning of large language models in natural language understanding.Communications of the ACM, 67(1): 110–120, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.194882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c5cf851-4d78-46c7-b1c0-22654c724981 · outbound

This paper cites Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?.

On Adversarial Robustness of Language Models in Transfer Learning Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.757245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27499e86-d4f6-4e14-b881-f3fc1f1d9e28 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

On Adversarial Robustness of Language Models in Transfer Learning Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.762992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f799334a-182d-4783-a6ea-2cd25b8249fb · outbound

This paper cites An Overview of Catastrophic AI Risks.

On Adversarial Robustness of Language Models in Transfer Learning An Overview of Catastrophic AI Risks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.769147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00c23be4-566b-4dc5-a7f1-6a89f0f1e6bb · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

On Adversarial Robustness of Language Models in Transfer Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.775047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dfa6ff3-af2b-4676-99bb-2445ba8cd82d · outbound

This paper cites Is bert really robust? a strong baseline for natural language attack on text classification and entailment.

On Adversarial Robustness of Language Models in Transfer Learning Is bert really robust? a strong baseline for natural language attack on text classification and entailment

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.174982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 14aede6e-ac54-46cd-b01e-ca321e8010c7 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

On Adversarial Robustness of Language Models in Transfer Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.785261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4b97f34-84b6-4e32-a520-24b45f60f994 · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

On Adversarial Robustness of Language Models in Transfer Learning An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.790706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94e244e0-f212-4654-bc2b-2e66fb17a77e · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

On Adversarial Robustness of Language Models in Transfer Learning Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.796178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6751f77-8198-46c3-b17e-fe516609bb4f · outbound

This paper cites Adversarial attacks and defenses in large language models: Old and new threats.

On Adversarial Robustness of Language Models in Transfer Learning Adversarial attacks and defenses in large language models: Old and new threats

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.144682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 216c8f98-aa3f-4389-b4e4-fc1ebcd6a62b · outbound

This paper cites Introducing mbib-the first media bias identification benchmark task and dataset collection.

On Adversarial Robustness of Language Models in Transfer Learning Introducing mbib-the first media bias identification benchmark task and dataset collection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.127597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a796de3e-304e-4073-96e7-9a2eb1513fde · outbound

This paper cites It is all about data: A survey on the effects of data on adversarial robustness.ACM Computing Surveys, 56(7):1–41, 2024.

On Adversarial Robustness of Language Models in Transfer Learning It is all about data: A survey on the effects of data on adversarial robustness.ACM Computing Surveys, 56(7):1–41, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:42.001464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5223ae79-5e8f-47b6-ab56-51e0d798337b · outbound

This paper cites Assessing Adversarial Robustness of Large Language Models: An Empirical Study.

On Adversarial Robustness of Language Models in Transfer Learning Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:41.816106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79f74199-6f23-4c0a-9917-2c8a5331cb09 · outbound

This paper cites Towards Improving Adversarial Training of NLP Models.

On Adversarial Robustness of Language Models in Transfer Learning Towards Improving Adversarial Training of NLP Models

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-08-10T23:21:41.821380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0221e06f-59c7-46b4-988c-a0d9d7d2b51b · inbound

Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study cites this paper.

Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study On Adversarial Robustness of Language Models in Transfer Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:15:03.193038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25ddf62b-575c-4849-a84d-de8a072c1b0c · inbound

Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study cites this paper.

Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study On Adversarial Robustness of Language Models in Transfer Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:42:19.229419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.