Paper Citation Record · LEDGER

Attacking interpretable NLP systems

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.16164.

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

pith.paper-citation-record.v1
2507.16164 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:22:32.069776Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2843b233-77a0-4cf5-bf92-ea3ac28c7353 · outbound

This paper cites A survey on sentiment analysis methods, applications, and challenges,.

Attacking interpretable NLP systems A survey on sentiment analysis methods, applications, and challenges,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:28.743887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8a9b13-9f90-41ed-84eb-8715919b2903 · outbound

This paper cites A survey of multilingual neural machine translation,.

Attacking interpretable NLP systems A survey of multilingual neural machine translation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.687593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df5ad70c-affb-4d68-bfc0-0e40a607f3a3 · outbound

This paper cites Recent advances in deep learning based dialogue systems: A systematic survey,.

Attacking interpretable NLP systems Recent advances in deep learning based dialogue systems: A systematic survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.666241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe982be1-65e6-4c5d-8121-219a0cbfc08d · outbound

This paper cites Parafuzz: An interpretability-driven technique for detecting poisoned samples in nlp,.

Attacking interpretable NLP systems Parafuzz: An interpretability-driven technique for detecting poisoned samples in nlp,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.644206Z

Source-reported events for the cited work

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Observation dc738f8d-eb6e-4086-98ca-b306b4fb81e7 · outbound

This paper cites Adversarial attacks on deep-learning models in natural language processing: A survey,.

Attacking interpretable NLP systems Adversarial attacks on deep-learning models in natural language processing: A survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.623105Z

Source-reported events for the cited work

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Observation f021a107-6356-4846-82cf-71b4cf95acff · outbound

This paper cites Adversarial nlp for social network applications: Attacks, defenses, and research directions,.

Attacking interpretable NLP systems Adversarial nlp for social network applications: Attacks, defenses, and research directions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.603125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d89cc26-2184-4063-a468-bbbfb000a843 · outbound

This paper cites A unified approach to interpreting model predictions,.

Attacking interpretable NLP systems A unified approach to interpreting model predictions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:29.147119Z

Source-reported events for the cited work

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Observation 296796fd-f942-4fa3-be40-f812004fc023 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Attacking interpretable NLP systems Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.558844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6952771e-df57-4d16-8e39-02b2523237c0 · outbound

This paper cites ” why should i trust you?.

Attacking interpretable NLP systems ” why should i trust you?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.519014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd795e8a-54ca-4556-9d51-e2ce5a586c88 · outbound

This paper cites Defending pre-trained language models from adversarial word substitution without performance sacrifice,.

Attacking interpretable NLP systems Defending pre-trained language models from adversarial word substitution without performance sacrifice,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.491190Z

Source-reported events for the cited work

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Observation c664373c-9845-43d3-b9ff-e5e8fc898bf0 · outbound

This paper cites Generating natural language adversarial examples through probability weighted word saliency,.

Attacking interpretable NLP systems Generating natural language adversarial examples through probability weighted word saliency,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.458102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95baf297-f83b-4ae5-b2eb-74fdc463eb0e · outbound

This paper cites Fasttextdodger: Decision-based adversarial attack against black-box nlp models with extremely high efficiency,.

Attacking interpretable NLP systems Fasttextdodger: Decision-based adversarial attack against black-box nlp models with extremely high efficiency,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.430723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 038a962e-2044-4ae3-a64b-7250f6ff873f · outbound

This paper cites Nuat- gan: Generating black-box natural universal adversarial triggers for text classifiers using generative adversarial networks,.

Attacking interpretable NLP systems Nuat- gan: Generating black-box natural universal adversarial triggers for text classifiers using generative adversarial networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.403390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24a59c35-fd20-4ff0-bc7b-5fcadc3d7886 · outbound

This paper cites Advedge: Optimizing adversarial perturbations against in- terpretable deep learning,.

Attacking interpretable NLP systems Advedge: Optimizing adversarial perturbations against in- terpretable deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.365377Z

Source-reported events for the cited work

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Observation 9eac6312-07be-4c66-bdf1-2885de531d5a · outbound

This paper cites Hardening interpretable deep learning systems: Investigat- ing adversarial threats and defenses,.

Attacking interpretable NLP systems Hardening interpretable deep learning systems: Investigat- ing adversarial threats and defenses,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.332836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f3827088-08fc-428b-b116-0bcda7867233 · outbound

This paper cites Black- box and target-specific attack against interpretable deep learning sys- tems,.

Attacking interpretable NLP systems Black- box and target-specific attack against interpretable deep learning sys- tems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.283296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e53f1e3-941b-45eb-8474-bdc5a97d3056 · outbound

This paper cites Singleadv: single-class target-specific attack against in- terpretable deep learning systems,.

Attacking interpretable NLP systems Singleadv: single-class target-specific attack against in- terpretable deep learning systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.256944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c20f54a-e2ce-4679-9e34-c98786363a27 · outbound

This paper cites A Survey of Black-Box Adversarial Attacks on Computer Vision Models.

Attacking interpretable NLP systems A Survey of Black-Box Adversarial Attacks on Computer Vision Models

Reference 18

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Source-reported events for the cited work

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Observation 3a39aa4d-4d03-4b15-80cd-55b0538c4b62 · outbound

This paper cites Seqvat: Virtual adversarial training for semi-supervised sequence labeling,.

Attacking interpretable NLP systems Seqvat: Virtual adversarial training for semi-supervised sequence labeling,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.229871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80e650d2-2434-4031-ad3b-f88c42d17226 · outbound

This paper cites Tianyu du, xiangyu liu, rong zhang, hui xue, and shouling ji. 2021. enhancing model robustness by incorporating adversarial knowledge into semantic representation,.

Attacking interpretable NLP systems Tianyu du, xiangyu liu, rong zhang, hui xue, and shouling ji. 2021. enhancing model robustness by incorporating adversarial knowledge into semantic representation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.207208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e454ce05-6188-47eb-896f-687a1fa5707a · outbound

This paper cites Robust Neural Machine Translation with Doubly Adversarial Inputs.

Attacking interpretable NLP systems Robust Neural Machine Translation with Doubly Adversarial Inputs

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:22:32.426683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e4b99ab-8cf6-46d8-8e24-a07bd496d083 · outbound

This paper cites A survey of adversarial defenses and robustness in NLP,.

Attacking interpretable NLP systems A survey of adversarial defenses and robustness in NLP,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.181004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c19afc8a-b126-4ca7-908b-e015ae01f55f · outbound

This paper cites Efficiently generating sentence-level textual adversarial examples with seq2seq stacked auto-encoder,.

Attacking interpretable NLP systems Efficiently generating sentence-level textual adversarial examples with seq2seq stacked auto-encoder,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.153217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b355bf8f-54a8-4300-b2cc-3a1b008ad1cc · outbound

This paper cites Joint character-level word embedding and adversarial stability training to defend adversarial text,.

Attacking interpretable NLP systems Joint character-level word embedding and adversarial stability training to defend adversarial text,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.123480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9df7dd87-e395-4903-9af1-580c1cedd3c9 · outbound

This paper cites Financial sentiment analysis: Techniques and applications,.

Attacking interpretable NLP systems Financial sentiment analysis: Techniques and applications,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.096562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4c1deac-5fba-4eb3-a71c-dc72c93d4b5b · outbound

This paper cites A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe?.

Attacking interpretable NLP systems A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe?

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.070348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34026e0a-d307-44f5-8f6a-d4645fd2ae59 · outbound

This paper cites Non- autoregressive machine translation with probabilistic context-free gram- mar,.

Attacking interpretable NLP systems Non- autoregressive machine translation with probabilistic context-free gram- mar,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.036783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 585e1302-45b5-4e91-9c20-d1456103c576 · outbound

This paper cites Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering,.

Attacking interpretable NLP systems Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:33.010503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c99eeabe-32d5-4aa5-91ec-281d75d0186c · outbound

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

Attacking interpretable NLP systems Is bert really robust? a strong baseline for natural language attack on text classification and entailment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.987163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8179913-3c6c-4c2f-9fde-eeb6f221c8f3 · outbound

This paper cites Language models are unsupervised multitask learners,.

Attacking interpretable NLP systems Language models are unsupervised multitask learners,

Reference 30

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Source-reported events for the cited work

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Observation da5596dc-5099-4e1f-975b-11edcdc542ea · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Attacking interpretable NLP systems Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.938434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 43565fcf-42e0-46e1-ae5f-288d549ccc5a · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Attacking interpretable NLP systems DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.035731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b9195c8-840f-4b5d-b7ff-a6ff8160eb8f · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

Attacking interpretable NLP systems ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.098740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6b9caad-0a3e-4132-864f-27ce1e7e43fb · outbound

This paper cites Canine: Pre-training an efficient tokenization-free encoder for language representation,.

Attacking interpretable NLP systems Canine: Pre-training an efficient tokenization-free encoder for language representation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.911399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b856a40-639c-4f76-bda6-353baa7372ef · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

Attacking interpretable NLP systems FNet: Mixing Tokens with Fourier Transforms

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.253454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d850fea9-9cdf-4b1e-8d93-6d538f2dcacf · outbound

This paper cites Unsupervised Cross-lingual Representation Learning at Scale.

Attacking interpretable NLP systems Unsupervised Cross-lingual Representation Learning at Scale

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.420580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b73a9f37-a138-4413-9f42-2b6956a604e6 · outbound

This paper cites A unified approach to interpreting model predictions,.

Attacking interpretable NLP systems A unified approach to interpreting model predictions,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.560674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427c0d93-1d23-4843-8e7f-4b030b027f58 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Attacking interpretable NLP systems Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T15:22:31.692085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3505eaa7-f59f-4adf-9a76-c5c80f54c333 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

Attacking interpretable NLP systems Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.816170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f9b0b4f-dead-4294-8f62-12d47d785c59 · outbound

This paper cites Character-level convolutional networks for text classification,.

Attacking interpretable NLP systems Character-level convolutional networks for text classification,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.670140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c6e48c2-e128-4065-92fe-6b5ecb09998e · outbound

This paper cites Textbugger: Generating adversarial text against real-world applications,.

Attacking interpretable NLP systems Textbugger: Generating adversarial text against real-world applications,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:22:32.555099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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