Best Practices and Lessons Learned on Synthetic Data Article Swipe
Ruibo Liu
,
Jerry Wei
,
Fangyu Liu
,
Chenglei Si
,
Yanzhe Zhang
,
Jinmeng Rao
,
Steven Zheng
,
Daiyi Peng
,
Diyi Yang
,
Denny Zhou
,
Andrew M. Dai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07503
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07503
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models.
Related Topics
Concepts
Synthetic data
Scarcity
Trustworthiness
Fidelity
Computer science
Data science
Data quality
Quality (philosophy)
Best practice
Language model
Artificial intelligence
Metric (unit)
Engineering
Computer security
Microeconomics
Epistemology
Management
Economics
Telecommunications
Operations management
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.07503
- https://arxiv.org/pdf/2404.07503
- OA Status
- green
- Cited By
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394781039
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394781039Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2404.07503Digital Object Identifier
- Title
-
Best Practices and Lessons Learned on Synthetic DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-11Full publication date if available
- Authors
-
Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, Andrew M. DaiList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.07503Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.07503Direct 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/2404.07503Direct OA link when available
- Concepts
-
Synthetic data, Scarcity, Trustworthiness, Fidelity, Computer science, Data science, Data quality, Quality (philosophy), Best practice, Language model, Artificial intelligence, Metric (unit), Engineering, Computer security, Microeconomics, Epistemology, Management, Economics, Telecommunications, Operations management, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 5Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4394781039 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2404.07503 |
| ids.doi | https://doi.org/10.48550/arxiv.2404.07503 |
| ids.openalex | https://openalex.org/W4394781039 |
| fwci | |
| type | preprint |
| title | Best Practices and Lessons Learned on Synthetic Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9768999814987183 |
| 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 | Topic Modeling |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C160920958 |
| concepts[0].level | 2 |
| concepts[0].score | 0.741506040096283 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7662746 |
| concepts[0].display_name | Synthetic data |
| concepts[1].id | https://openalex.org/C109747225 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6967135071754456 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q815758 |
| concepts[1].display_name | Scarcity |
| concepts[2].id | https://openalex.org/C153701036 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6895353198051453 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q659974 |
| concepts[2].display_name | Trustworthiness |
| concepts[3].id | https://openalex.org/C2776459999 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6787741780281067 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2119376 |
| concepts[3].display_name | Fidelity |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.674260139465332 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C2522767166 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5517324805259705 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[5].display_name | Data science |
| concepts[6].id | https://openalex.org/C24756922 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4771506190299988 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1757694 |
| concepts[6].display_name | Data quality |
| concepts[7].id | https://openalex.org/C2779530757 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46304208040237427 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1207505 |
| concepts[7].display_name | Quality (philosophy) |
| concepts[8].id | https://openalex.org/C184356942 |
| concepts[8].level | 2 |
| concepts[8].score | 0.42174533009529114 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q830382 |
| concepts[8].display_name | Best practice |
| concepts[9].id | https://openalex.org/C137293760 |
| concepts[9].level | 2 |
| concepts[9].score | 0.42000916600227356 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3621696 |
| concepts[9].display_name | Language model |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.36118102073669434 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C176217482 |
| concepts[11].level | 2 |
| concepts[11].score | 0.24061009287834167 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q860554 |
| concepts[11].display_name | Metric (unit) |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.1319851279258728 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C38652104 |
| concepts[13].level | 1 |
| concepts[13].score | 0.12408328056335449 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[13].display_name | Computer security |
| concepts[14].id | https://openalex.org/C175444787 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q39072 |
| concepts[14].display_name | Microeconomics |
| concepts[15].id | https://openalex.org/C111472728 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[15].display_name | Epistemology |
| concepts[16].id | https://openalex.org/C187736073 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[16].display_name | Management |
| concepts[17].id | https://openalex.org/C162324750 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[17].display_name | Economics |
| concepts[18].id | https://openalex.org/C76155785 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[18].display_name | Telecommunications |
| concepts[19].id | https://openalex.org/C21547014 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1423657 |
| concepts[19].display_name | Operations management |
| concepts[20].id | https://openalex.org/C138885662 |
| concepts[20].level | 0 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[20].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/synthetic-data |
| keywords[0].score | 0.741506040096283 |
| keywords[0].display_name | Synthetic data |
| keywords[1].id | https://openalex.org/keywords/scarcity |
| keywords[1].score | 0.6967135071754456 |
| keywords[1].display_name | Scarcity |
| keywords[2].id | https://openalex.org/keywords/trustworthiness |
| keywords[2].score | 0.6895353198051453 |
| keywords[2].display_name | Trustworthiness |
| keywords[3].id | https://openalex.org/keywords/fidelity |
| keywords[3].score | 0.6787741780281067 |
| keywords[3].display_name | Fidelity |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.674260139465332 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/data-science |
| keywords[5].score | 0.5517324805259705 |
| keywords[5].display_name | Data science |
| keywords[6].id | https://openalex.org/keywords/data-quality |
| keywords[6].score | 0.4771506190299988 |
| keywords[6].display_name | Data quality |
| keywords[7].id | https://openalex.org/keywords/quality |
| keywords[7].score | 0.46304208040237427 |
| keywords[7].display_name | Quality (philosophy) |
| keywords[8].id | https://openalex.org/keywords/best-practice |
| keywords[8].score | 0.42174533009529114 |
| keywords[8].display_name | Best practice |
| keywords[9].id | https://openalex.org/keywords/language-model |
| keywords[9].score | 0.42000916600227356 |
| keywords[9].display_name | Language model |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.36118102073669434 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/metric |
| keywords[11].score | 0.24061009287834167 |
| keywords[11].display_name | Metric (unit) |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.1319851279258728 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/computer-security |
| keywords[13].score | 0.12408328056335449 |
| keywords[13].display_name | Computer security |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2404.07503 |
| 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/2404.07503 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| 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/2404.07503 |
| locations[1].id | doi:10.48550/arxiv.2404.07503 |
| 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.2404.07503 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5023339675 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5163-966X |
| authorships[0].author.display_name | Ruibo Liu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Liu, Ruibo |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5021855913 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8802-7221 |
| authorships[1].author.display_name | Jerry Wei |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wei, Jerry |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5009154638 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Fangyu Liu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Liu, Fangyu |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101336165 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chenglei Si |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Si, Chenglei |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5109704845 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-1874-2622 |
| authorships[4].author.display_name | Yanzhe Zhang |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Zhang, Yanzhe |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5023215843 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-2370-5129 |
| authorships[5].author.display_name | Jinmeng Rao |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Rao, Jinmeng |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5112919416 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Steven Zheng |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Zheng, Steven |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5104280352 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Daiyi Peng |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Peng, Daiyi |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5089413311 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-1220-3983 |
| authorships[8].author.display_name | Diyi Yang |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Yang, Diyi |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5061512999 |
| authorships[9].author.orcid | |
| authorships[9].author.display_name | Denny Zhou |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Zhou, Denny |
| authorships[9].is_corresponding | False |
| authorships[10].author.id | https://openalex.org/A5101597225 |
| authorships[10].author.orcid | https://orcid.org/0009-0007-9200-8577 |
| authorships[10].author.display_name | Andrew M. Dai |
| authorships[10].author_position | last |
| authorships[10].raw_author_name | Dai, Andrew M. |
| authorships[10].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/2404.07503 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-04-13T00:00:00 |
| display_name | Best Practices and Lessons Learned on Synthetic Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9768999814987183 |
| 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 | Topic Modeling |
| related_works | https://openalex.org/W1571141552, https://openalex.org/W4391636338, https://openalex.org/W4386067343, https://openalex.org/W4294250823, https://openalex.org/W2093086151, https://openalex.org/W55936454, https://openalex.org/W2095572632, https://openalex.org/W2944249426, https://openalex.org/W4294609170, https://openalex.org/W129955550 |
| cited_by_count | 14 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2404.07503 |
| 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/2404.07503 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| 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/2404.07503 |
| primary_location.id | pmh:oai:arXiv.org:2404.07503 |
| 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/2404.07503 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| 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/2404.07503 |
| publication_date | 2024-04-11 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 35 |
| abstract_inverted_index.AI | 3 |
| abstract_inverted_index.We | 62, 84 |
| abstract_inverted_index.an | 49 |
| abstract_inverted_index.as | 34 |
| abstract_inverted_index.be | 17 |
| abstract_inverted_index.by | 38 |
| abstract_inverted_index.of | 2, 9, 51, 77, 91 |
| abstract_inverted_index.on | 6 |
| abstract_inverted_index.to | 19, 22, 69, 94 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 12, 27, 59, 73, 82, 99 |
| abstract_inverted_index.art | 68 |
| abstract_inverted_index.can | 16 |
| abstract_inverted_index.due | 21 |
| abstract_inverted_index.for | 88 |
| abstract_inverted_index.has | 32 |
| abstract_inverted_index.its | 56, 71, 79 |
| abstract_inverted_index.the | 7, 75, 86 |
| abstract_inverted_index.use | 90 |
| abstract_inverted_index.This | 46 |
| abstract_inverted_index.data | 23, 31, 41, 53, 93 |
| abstract_inverted_index.from | 66 |
| abstract_inverted_index.high | 28 |
| abstract_inverted_index.more | 96 |
| abstract_inverted_index.need | 87 |
| abstract_inverted_index.that | 42 |
| abstract_inverted_index.build | 95 |
| abstract_inverted_index.paper | 47 |
| abstract_inverted_index.prior | 67 |
| abstract_inverted_index.which | 15 |
| abstract_inverted_index.costs. | 29 |
| abstract_inverted_index.future | 60 |
| abstract_inverted_index.large, | 10 |
| abstract_inverted_index.mimics | 43 |
| abstract_inverted_index.models | 4 |
| abstract_inverted_index.obtain | 20 |
| abstract_inverted_index.relies | 5 |
| abstract_inverted_index.emerged | 33 |
| abstract_inverted_index.models. | 102 |
| abstract_inverted_index.present | 63 |
| abstract_inverted_index.privacy | 25 |
| abstract_inverted_index.success | 1 |
| abstract_inverted_index.diverse, | 11 |
| abstract_inverted_index.ensuring | 78 |
| abstract_inverted_index.evidence | 65 |
| abstract_inverted_index.language | 101 |
| abstract_inverted_index.overview | 50 |
| abstract_inverted_index.provides | 48 |
| abstract_inverted_index.solution | 37 |
| abstract_inverted_index.Synthetic | 30 |
| abstract_inverted_index.concerns, | 26 |
| abstract_inverted_index.datasets, | 14 |
| abstract_inverted_index.emphasize | 85 |
| abstract_inverted_index.empirical | 64 |
| abstract_inverted_index.fidelity, | 81 |
| abstract_inverted_index.highlight | 74 |
| abstract_inverted_index.patterns. | 45 |
| abstract_inverted_index.powerful, | 97 |
| abstract_inverted_index.promising | 36 |
| abstract_inverted_index.research, | 54 |
| abstract_inverted_index.scarcity, | 24 |
| abstract_inverted_index.synthetic | 52, 92 |
| abstract_inverted_index.artificial | 40 |
| abstract_inverted_index.discussing | 55 |
| abstract_inverted_index.generating | 39 |
| abstract_inverted_index.importance | 76 |
| abstract_inverted_index.inclusive, | 98 |
| abstract_inverted_index.real-world | 44 |
| abstract_inverted_index.challenges, | 58 |
| abstract_inverted_index.challenging | 18 |
| abstract_inverted_index.demonstrate | 70 |
| abstract_inverted_index.directions. | 61 |
| abstract_inverted_index.factuality, | 80 |
| abstract_inverted_index.responsible | 89 |
| abstract_inverted_index.trustworthy | 100 |
| abstract_inverted_index.availability | 8 |
| abstract_inverted_index.high-quality | 13 |
| abstract_inverted_index.applications, | 57 |
| abstract_inverted_index.effectiveness | 72 |
| abstract_inverted_index.unbiasedness. | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile |