Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks | Paper Citation Record

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This paper cites write newline.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks write newline

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This paper cites Fast and precise certification of transformers.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Fast and precise certification of transformers

Reference 2

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This paper cites Mitigating backdoor attacks in lstm-based text classification systems by backdoor keyword identification.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Mitigating backdoor attacks in lstm-based text classification systems by backdoor keyword identification

Reference 3

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This paper cites Badpre: Task-agnostic backdoor attacks to pre-trained nlp foundation models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Badpre: Task-agnostic backdoor attacks to pre-trained nlp foundation models

Reference 4

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This paper cites Badnl: Backdoor attacks against nlp models with semantic-preserving improvements.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Badnl: Backdoor attacks against nlp models with semantic-preserving improvements

Reference 5

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This paper cites SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer

Reference 6

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This paper cites Certified adversarial robustness via randomized smoothing.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified adversarial robustness via randomized smoothing

Reference 7

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This paper cites A unified evaluation of textual backdoor learning: Frameworks and benchmarks.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks A unified evaluation of textual backdoor learning: Frameworks and benchmarks

Reference 8

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This paper cites A backdoor attack against lstm-based text classification systems.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks A backdoor attack against lstm-based text classification systems

Reference 9

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This paper cites A technique for computer detection and correction of spelling errors.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks A technique for computer detection and correction of spelling errors

Reference 10

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This paper cites Cert-rnn: Towards certifying the robustness of recurrent neural networks.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Cert-rnn: Towards certifying the robustness of recurrent neural networks

Reference 11

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This paper cites The Llama 3 Herd of Models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks The Llama 3 Herd of Models

Reference 12

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Large scale crowdsourcing and characterization of twitter abusive behavior

Reference 13

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This paper cites Threats to Pre-trained Language Models: Survey and Taxonomy.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Threats to Pre-trained Language Models: Survey and Taxonomy

Reference 14

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This paper cites Lora: Low-rank adaptation of large language models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Lora: Low-rank adaptation of large language models

Reference 15

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This paper cites Achieving verified robustness to symbol substitutions via interval bound propagation.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Achieving verified robustness to symbol substitutions via interval bound propagation

Reference 16

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This paper cites Advancing the Robustness of Large Language Models through Self-Denoised Smoothing.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Advancing the Robustness of Large Language Models through Self-Denoised Smoothing

Reference 17

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This paper cites Certified robustness to adversarial word substitutions.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified robustness to adversarial word substitutions

Reference 18

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

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 19

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks On information and sufficiency

Reference 20

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Weight poisoning attacks on pretrained models

Reference 21

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This paper cites Newsweeder: Learning to filter netnews.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Newsweeder: Learning to filter netnews

Reference 22

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This paper cites Binary codes capable of correcting deletions, insertions, and reversals.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Binary codes capable of correcting deletions, insertions, and reversals

Reference 23

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This paper cites Backdoor attacks on pre-trained models by layerwise weight poisoning.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Backdoor attacks on pre-trained models by layerwise weight poisoning

Reference 24

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 25

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This paper cites CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models

Reference 26

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Learning word vectors for sentiment analysis

Reference 27

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This paper cites TextGuard: Provable Defense against Backdoor Attacks on Text Classification.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks TextGuard: Provable Defense against Backdoor Attacks on Text Classification

Reference 28

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Onion: A simple and effective defense against textual backdoor attacks

Reference 29

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Hidden killer: Invisible textual backdoor attacks with syntactic trigger

Reference 30

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Turn the combination lock: Learnable textual backdoor attacks via word substitution

Reference 31

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Backdoor pre-trained models can transfer to all

Reference 32

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

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Recursive deep models for semantic compositionality over a sentiment treebank

Reference 33

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks On Certifying Robustness against Backdoor Attacks via Randomized Smoothing

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified robustness to word substitution attack with differential privacy

Reference 36

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Robustness-aware word embedding improves certified robustness to adversarial word substitutions

Reference 37

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Rab: Provable robustness against backdoor attacks

Reference 38

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Crfl: Certifiably robust federated learning against backdoor attacks

Reference 39

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Bite: Textual backdoor attacks with iterative trigger injection

Reference 40

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Be careful about poisoned word embeddings: Exploring the vulnerability of the embedding layers in nlp models

Reference 41

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Rap: Robustness-aware perturbations for defending against backdoor attacks on nlp models

Reference 42

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Safer: A structure-free approach for certified robustness to adversarial word substitutions

Reference 43

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This paper cites Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval).

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)

Reference 44

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This paper cites Certified robustness to text adversarial attacks by randomized [mask].

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified robustness to text adversarial attacks by randomized [mask]

Reference 45

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Character-level convolutional networks for text classification

Reference 46

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Trojaning language models for fun and profit

Reference 47

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This paper cites Text-crs: A generalized certified robustness framework against textual adversarial attacks.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Text-crs: A generalized certified robustness framework against textual adversarial attacks

Reference 48

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This paper cites Random smooth-based certified defense against text adversarial attack.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Random smooth-based certified defense against text adversarial attack

Reference 49

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This paper cites Certified Robustness for Large Language Models with Self-Denoising.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified Robustness for Large Language Models with Self-Denoising

Reference 50

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Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Certified robustness against natural language attacks by causal intervention

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:43:25.228939Z

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Observation 9909c6b8-19a9-45bd-92c7-d908ade9326e · outbound

This paper cites Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T17:43:24.844859Z

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Observation 0bc981d4-ce1b-4f45-9926-d7fe02f2ad0f · outbound

This paper cites Moderate-fitting as a natural backdoor defender for pre-trained language models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Moderate-fitting as a natural backdoor defender for pre-trained language models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:43:25.214058Z

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Observation 3e495699-67c5-4fed-b617-b36584aed68a · outbound

This paper cites @esa (Ref.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks @esa (Ref

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T17:43:24.852617Z

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Observation 3539ce4e-c4ef-42ed-bbd3-eccf26facc59 · outbound

This paper cites an unresolved cited work.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T17:43:24.856921Z

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Observation ce4d46fb-e9dc-4ef1-820f-3fe58b5014c4 · outbound

This paper cites Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T17:43:24.861099Z

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