From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework Article Swipe
Yang‐Yi Chen
,
Hongcheng Gao
,
Ganqu Cui
,
Lifan Yuan
,
Dehan Kong
,
Hanlu Wu
,
Ning Shi
,
Bo Yuan
,
Longtao Huang
,
Hui Xue
,
Zhiyuan Liu
,
Maosong Sun
,
Heng Ji
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18503
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.18503
Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework. The code will be made public at \url{https://github.com/thunlp/RobTest}.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.18503
- https://arxiv.org/pdf/2305.18503
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378942430
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378942430Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2305.18503Digital Object Identifier
- Title
-
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-29Full publication date if available
- Authors
-
Yang‐Yi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng JiList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.18503Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.18503Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2305.18503Direct OA link when available
- Concepts
-
Robustness (evolution), Adversarial system, Computer science, Exploit, Artificial intelligence, Machine learning, Data mining, Computer security, Biochemistry, Chemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4378942430 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2305.18503 |
| ids.doi | https://doi.org/10.48550/arxiv.2305.18503 |
| ids.openalex | https://openalex.org/W4378942430 |
| fwci | |
| type | preprint |
| title | From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11689 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.995199978351593 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Adversarial Robustness in Machine Learning |
| topics[1].id | https://openalex.org/T10260 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9478999972343445 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1710 |
| topics[1].subfield.display_name | Information Systems |
| topics[1].display_name | Software Engineering Research |
| topics[2].id | https://openalex.org/T11948 |
| topics[2].field.id | https://openalex.org/fields/25 |
| topics[2].field.display_name | Materials Science |
| topics[2].score | 0.9275000095367432 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2505 |
| topics[2].subfield.display_name | Materials Chemistry |
| topics[2].display_name | Machine Learning in Materials Science |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C63479239 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8456802368164062 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[0].display_name | Robustness (evolution) |
| concepts[1].id | https://openalex.org/C37736160 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8334332704544067 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1801315 |
| concepts[1].display_name | Adversarial system |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7825472354888916 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C165696696 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7668389081954956 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11287 |
| concepts[3].display_name | Exploit |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.48825836181640625 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.46973973512649536 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.323577344417572 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C38652104 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2265380620956421 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[7].display_name | Computer security |
| concepts[8].id | https://openalex.org/C55493867 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[8].display_name | Biochemistry |
| concepts[9].id | https://openalex.org/C185592680 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[9].display_name | Chemistry |
| concepts[10].id | https://openalex.org/C104317684 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[10].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/robustness |
| keywords[0].score | 0.8456802368164062 |
| keywords[0].display_name | Robustness (evolution) |
| keywords[1].id | https://openalex.org/keywords/adversarial-system |
| keywords[1].score | 0.8334332704544067 |
| keywords[1].display_name | Adversarial system |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7825472354888916 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/exploit |
| keywords[3].score | 0.7668389081954956 |
| keywords[3].display_name | Exploit |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.48825836181640625 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.46973973512649536 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.323577344417572 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/computer-security |
| keywords[7].score | 0.2265380620956421 |
| keywords[7].display_name | Computer security |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2305.18503 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2305.18503 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2305.18503 |
| locations[1].id | doi:10.48550/arxiv.2305.18503 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2305.18503 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5005441169 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8488-635X |
| authorships[0].author.display_name | Yang‐Yi Chen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chen, Yangyi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5110370692 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Hongcheng Gao |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gao, Hongcheng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5102542015 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6385-8547 |
| authorships[2].author.display_name | Ganqu Cui |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Cui, Ganqu |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5081648076 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Lifan Yuan |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yuan, Lifan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5021394202 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4626-5675 |
| authorships[4].author.display_name | Dehan Kong |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Kong, Dehan |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5109610287 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Hanlu Wu |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Wu, Hanlu |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5100651673 |
| authorships[6].author.orcid | https://orcid.org/0009-0008-5340-3504 |
| authorships[6].author.display_name | Ning Shi |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Shi, Ning |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5032061647 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-2169-0007 |
| authorships[7].author.display_name | Bo Yuan |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Yuan, Bo |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5058197951 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-0517-1592 |
| authorships[8].author.display_name | Longtao Huang |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Huang, Longtao |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5100337747 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-2093-2839 |
| authorships[9].author.display_name | Hui Xue |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Xue, Hui |
| authorships[9].is_corresponding | False |
| authorships[10].author.id | https://openalex.org/A5100320723 |
| authorships[10].author.orcid | https://orcid.org/0000-0002-7709-2543 |
| authorships[10].author.display_name | Zhiyuan Liu |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Liu, Zhiyuan |
| authorships[10].is_corresponding | False |
| authorships[11].author.id | https://openalex.org/A5046448314 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-6011-6115 |
| authorships[11].author.display_name | Maosong Sun |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Sun, Maosong |
| authorships[11].is_corresponding | False |
| authorships[12].author.id | https://openalex.org/A5103178893 |
| authorships[12].author.orcid | https://orcid.org/0000-0002-7954-7994 |
| authorships[12].author.display_name | Heng Ji |
| authorships[12].author_position | last |
| authorships[12].raw_author_name | Ji, Heng |
| authorships[12].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2305.18503 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11689 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.995199978351593 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Adversarial Robustness in Machine Learning |
| related_works | https://openalex.org/W17155033, https://openalex.org/W3207760230, https://openalex.org/W1496222301, https://openalex.org/W1590307681, https://openalex.org/W2536018345, https://openalex.org/W4312814274, https://openalex.org/W4285370786, https://openalex.org/W2296488620, https://openalex.org/W2502115930, https://openalex.org/W2358353312 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2305.18503 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2305.18503 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2305.18503 |
| primary_location.id | pmh:oai:arXiv.org:2305.18503 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2305.18503 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2305.18503 |
| publication_date | 2023-05-29 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 64, 139, 154 |
| abstract_inverted_index.In | 56, 149 |
| abstract_inverted_index.To | 82 |
| abstract_inverted_index.We | 137 |
| abstract_inverted_index.at | 185 |
| abstract_inverted_index.be | 182 |
| abstract_inverted_index.by | 7 |
| abstract_inverted_index.in | 22, 176 |
| abstract_inverted_index.is | 27 |
| abstract_inverted_index.of | 40, 46, 79, 129, 157, 164, 173 |
| abstract_inverted_index.on | 94 |
| abstract_inverted_index.to | 13, 61, 74, 102, 132, 160 |
| abstract_inverted_index.up | 63 |
| abstract_inverted_index.we | 59, 87, 110, 124, 152 |
| abstract_inverted_index.The | 16, 179 |
| abstract_inverted_index.aim | 60 |
| abstract_inverted_index.and | 52, 97, 118, 168 |
| abstract_inverted_index.but | 10 |
| abstract_inverted_index.can | 3 |
| abstract_inverted_index.for | 32, 106 |
| abstract_inverted_index.may | 43 |
| abstract_inverted_index.our | 144, 150, 165 |
| abstract_inverted_index.set | 62 |
| abstract_inverted_index.the | 14, 37, 77, 84, 99, 112, 126, 134, 162, 171, 177 |
| abstract_inverted_index.use | 125 |
| abstract_inverted_index.Then | 109 |
| abstract_inverted_index.arms | 20 |
| abstract_inverted_index.code | 180 |
| abstract_inverted_index.each | 107, 174 |
| abstract_inverted_index.made | 183 |
| abstract_inverted_index.race | 21 |
| abstract_inverted_index.show | 170 |
| abstract_inverted_index.that | 142 |
| abstract_inverted_index.this | 57 |
| abstract_inverted_index.will | 181 |
| abstract_inverted_index.(NLP) | 26 |
| abstract_inverted_index.above | 85 |
| abstract_inverted_index.based | 93 |
| abstract_inverted_index.first | 88 |
| abstract_inverted_index.model | 95 |
| abstract_inverted_index.under | 120 |
| abstract_inverted_index.adding | 8 |
| abstract_inverted_index.degree | 128 |
| abstract_inverted_index.issues | 45 |
| abstract_inverted_index.models | 159 |
| abstract_inverted_index.paper, | 58 |
| abstract_inverted_index.public | 184 |
| abstract_inverted_index.sample | 135 |
| abstract_inverted_index.Natural | 23 |
| abstract_inverted_index.RoBERTa | 158 |
| abstract_inverted_index.RobTest | 141 |
| abstract_inverted_index.Textual | 0 |
| abstract_inverted_index.address | 83 |
| abstract_inverted_index.attacks | 2 |
| abstract_inverted_index.conduct | 153 |
| abstract_inverted_index.control | 133 |
| abstract_inverted_index.exhibit | 44 |
| abstract_inverted_index.exploit | 76 |
| abstract_inverted_index.further | 75, 169 |
| abstract_inverted_index.inputs. | 15 |
| abstract_inverted_index.invalid | 53 |
| abstract_inverted_index.models' | 5 |
| abstract_inverted_index.samples | 105, 131 |
| abstract_inverted_index.specify | 98 |
| abstract_inverted_index.toolkit | 140 |
| abstract_inverted_index.towards | 71 |
| abstract_inverted_index.unified | 65 |
| abstract_inverted_index.Finally, | 123 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.Language | 24 |
| abstract_inverted_index.attacks. | 81 |
| abstract_inverted_index.demands. | 122 |
| abstract_inverted_index.discover | 4 |
| abstract_inverted_index.existing | 38 |
| abstract_inverted_index.generate | 103 |
| abstract_inverted_index.metrics, | 119 |
| abstract_inverted_index.practice | 39 |
| abstract_inverted_index.realizes | 143 |
| abstract_inverted_index.samples. | 55 |
| abstract_inverted_index.settings | 117 |
| abstract_inverted_index.shifting | 70 |
| abstract_inverted_index.valuable | 30 |
| abstract_inverted_index.algorithm | 101 |
| abstract_inverted_index.automatic | 33, 66, 145 |
| abstract_inverted_index.component | 175 |
| abstract_inverted_index.determine | 89 |
| abstract_inverted_index.establish | 111 |
| abstract_inverted_index.implement | 138 |
| abstract_inverted_index.including | 115 |
| abstract_inverted_index.protocol, | 51, 114 |
| abstract_inverted_index.providing | 29 |
| abstract_inverted_index.realistic | 121 |
| abstract_inverted_index.validity. | 136 |
| abstract_inverted_index.Processing | 25 |
| abstract_inverted_index.advantages | 78 |
| abstract_inverted_index.dimension. | 108 |
| abstract_inverted_index.dimensions | 92 |
| abstract_inverted_index.evaluation | 42, 50, 68, 73, 91, 113, 116, 147, 156, 166 |
| abstract_inverted_index.framework, | 69, 167 |
| abstract_inverted_index.framework. | 148, 178 |
| abstract_inverted_index.misleading | 11 |
| abstract_inverted_index.reasonable | 100 |
| abstract_inverted_index.robustness | 34, 41, 67, 90, 146, 155 |
| abstract_inverted_index.techniques | 31 |
| abstract_inverted_index.weaknesses | 6 |
| abstract_inverted_index.adversarial | 1, 18, 54, 80, 104, 130 |
| abstract_inverted_index.challenges, | 86 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.evaluation, | 48 |
| abstract_inverted_index.evaluation. | 35 |
| abstract_inverted_index.impractical | 49 |
| abstract_inverted_index.rationality | 172 |
| abstract_inverted_index.capabilities | 96 |
| abstract_inverted_index.experiments, | 151 |
| abstract_inverted_index.long-lasting | 17 |
| abstract_inverted_index.perturbation | 127 |
| abstract_inverted_index.effectiveness | 163 |
| abstract_inverted_index.model-centric | 72 |
| abstract_inverted_index.perturbations | 12 |
| abstract_inverted_index.incomprehensive | 47 |
| abstract_inverted_index.algorithm-centric, | 28 |
| abstract_inverted_index.attack-and-defense | 19 |
| abstract_inverted_index.semantic-preserved | 9 |
| abstract_inverted_index.\url{https://github.com/thunlp/RobTest}. | 186 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |