What Makes Pre-trained Language Models Better Zero-shot Learners? Article Swipe
Jinghui Lu
,
Dongsheng Zhu
,
Weidong Han
,
Rui Zhao
,
Brian Mac Namee
,
Fei Tan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.acl-long.128
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.acl-long.128
Current methods for prompt learning in zero-shot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a real-world zero-shot scenario of practical relevance, no labelled data is available. Thus, we propose a simple yet effective method for screening reasonable prompt templates in zero-shot text classification: Perplexity Selection (Perplection). We hypothesize that language discrepancy can be used to measure the efficacy of prompt templates, and thereby develop a substantiated perplexity-based scheme allowing for forecasting the performance of prompt templates in advance. Experiments show that our method leads to improved prediction performance in a realistic zero-shot setting, eliminating the need for any labelled examples.
Related Topics
Concepts
Perplexity
Computer science
Template
Relevance (law)
Zero (linguistics)
Shot (pellet)
Artificial intelligence
Language model
Measure (data warehouse)
Set (abstract data type)
Scheme (mathematics)
One shot
Machine learning
Simple (philosophy)
Ideal (ethics)
Data mining
Mathematics
Programming language
Engineering
Political science
Philosophy
Mechanical engineering
Mathematical analysis
Organic chemistry
Epistemology
Linguistics
Chemistry
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2023.acl-long.128
- https://aclanthology.org/2023.acl-long.128.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385571701
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385571701Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2023.acl-long.128Digital Object Identifier
- Title
-
What Makes Pre-trained Language Models Better Zero-shot Learners?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Jinghui Lu, Dongsheng Zhu, Weidong Han, Rui Zhao, Brian Mac Namee, Fei TanList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2023.acl-long.128Publisher landing page
- PDF URL
-
https://aclanthology.org/2023.acl-long.128.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://aclanthology.org/2023.acl-long.128.pdfDirect OA link when available
- Concepts
-
Perplexity, Computer science, Template, Relevance (law), Zero (linguistics), Shot (pellet), Artificial intelligence, Language model, Measure (data warehouse), Set (abstract data type), Scheme (mathematics), One shot, Machine learning, Simple (philosophy), Ideal (ethics), Data mining, Mathematics, Programming language, Engineering, Political science, Philosophy, Mechanical engineering, Mathematical analysis, Organic chemistry, Epistemology, Linguistics, Chemistry, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385571701 |
|---|---|
| doi | https://doi.org/10.18653/v1/2023.acl-long.128 |
| ids.doi | https://doi.org/10.18653/v1/2023.acl-long.128 |
| ids.openalex | https://openalex.org/W4385571701 |
| fwci | 1.27721444 |
| type | article |
| title | What Makes Pre-trained Language Models Better Zero-shot Learners? |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10181 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9933000206947327 |
| 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 | Natural Language Processing Techniques |
| topics[1].id | https://openalex.org/T13629 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9919999837875366 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Text Readability and Simplification |
| topics[2].id | https://openalex.org/T10028 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9799000024795532 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Topic Modeling |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C100279451 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9040166735649109 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q372193 |
| concepts[0].display_name | Perplexity |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.8020878434181213 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C82714645 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6542115807533264 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q438331 |
| concepts[2].display_name | Template |
| concepts[3].id | https://openalex.org/C158154518 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6288127899169922 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7310970 |
| concepts[3].display_name | Relevance (law) |
| concepts[4].id | https://openalex.org/C2780813799 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6285942196846008 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3274237 |
| concepts[4].display_name | Zero (linguistics) |
| concepts[5].id | https://openalex.org/C2778344882 |
| concepts[5].level | 2 |
| concepts[5].score | 0.588228166103363 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q278938 |
| concepts[5].display_name | Shot (pellet) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5834453701972961 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C137293760 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5534048676490784 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3621696 |
| concepts[7].display_name | Language model |
| concepts[8].id | https://openalex.org/C2780009758 |
| concepts[8].level | 2 |
| concepts[8].score | 0.49834275245666504 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q6804172 |
| concepts[8].display_name | Measure (data warehouse) |
| concepts[9].id | https://openalex.org/C177264268 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4980940818786621 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[9].display_name | Set (abstract data type) |
| concepts[10].id | https://openalex.org/C77618280 |
| concepts[10].level | 2 |
| concepts[10].score | 0.47259458899497986 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1155772 |
| concepts[10].display_name | Scheme (mathematics) |
| concepts[11].id | https://openalex.org/C2992734406 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4554630517959595 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413267 |
| concepts[11].display_name | One shot |
| concepts[12].id | https://openalex.org/C119857082 |
| concepts[12].level | 1 |
| concepts[12].score | 0.4518463611602783 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[12].display_name | Machine learning |
| concepts[13].id | https://openalex.org/C2780586882 |
| concepts[13].level | 2 |
| concepts[13].score | 0.4310082793235779 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7520643 |
| concepts[13].display_name | Simple (philosophy) |
| concepts[14].id | https://openalex.org/C2776639384 |
| concepts[14].level | 2 |
| concepts[14].score | 0.4217422902584076 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q840396 |
| concepts[14].display_name | Ideal (ethics) |
| concepts[15].id | https://openalex.org/C124101348 |
| concepts[15].level | 1 |
| concepts[15].score | 0.2284134030342102 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[15].display_name | Data mining |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.10845485329627991 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| concepts[17].id | https://openalex.org/C199360897 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0720197856426239 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[17].display_name | Programming language |
| concepts[18].id | https://openalex.org/C127413603 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[18].display_name | Engineering |
| concepts[19].id | https://openalex.org/C17744445 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[19].display_name | Political science |
| 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 |
| concepts[21].id | https://openalex.org/C78519656 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[21].display_name | Mechanical engineering |
| concepts[22].id | https://openalex.org/C134306372 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[22].display_name | Mathematical analysis |
| concepts[23].id | https://openalex.org/C178790620 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q11351 |
| concepts[23].display_name | Organic chemistry |
| concepts[24].id | https://openalex.org/C111472728 |
| concepts[24].level | 1 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[24].display_name | Epistemology |
| concepts[25].id | https://openalex.org/C41895202 |
| concepts[25].level | 1 |
| concepts[25].score | 0.0 |
| concepts[25].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[25].display_name | Linguistics |
| concepts[26].id | https://openalex.org/C185592680 |
| concepts[26].level | 0 |
| concepts[26].score | 0.0 |
| concepts[26].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[26].display_name | Chemistry |
| concepts[27].id | https://openalex.org/C199539241 |
| concepts[27].level | 1 |
| concepts[27].score | 0.0 |
| concepts[27].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[27].display_name | Law |
| keywords[0].id | https://openalex.org/keywords/perplexity |
| keywords[0].score | 0.9040166735649109 |
| keywords[0].display_name | Perplexity |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.8020878434181213 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/template |
| keywords[2].score | 0.6542115807533264 |
| keywords[2].display_name | Template |
| keywords[3].id | https://openalex.org/keywords/relevance |
| keywords[3].score | 0.6288127899169922 |
| keywords[3].display_name | Relevance (law) |
| keywords[4].id | https://openalex.org/keywords/zero |
| keywords[4].score | 0.6285942196846008 |
| keywords[4].display_name | Zero (linguistics) |
| keywords[5].id | https://openalex.org/keywords/shot |
| keywords[5].score | 0.588228166103363 |
| keywords[5].display_name | Shot (pellet) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.5834453701972961 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/language-model |
| keywords[7].score | 0.5534048676490784 |
| keywords[7].display_name | Language model |
| keywords[8].id | https://openalex.org/keywords/measure |
| keywords[8].score | 0.49834275245666504 |
| keywords[8].display_name | Measure (data warehouse) |
| keywords[9].id | https://openalex.org/keywords/set |
| keywords[9].score | 0.4980940818786621 |
| keywords[9].display_name | Set (abstract data type) |
| keywords[10].id | https://openalex.org/keywords/scheme |
| keywords[10].score | 0.47259458899497986 |
| keywords[10].display_name | Scheme (mathematics) |
| keywords[11].id | https://openalex.org/keywords/one-shot |
| keywords[11].score | 0.4554630517959595 |
| keywords[11].display_name | One shot |
| keywords[12].id | https://openalex.org/keywords/machine-learning |
| keywords[12].score | 0.4518463611602783 |
| keywords[12].display_name | Machine learning |
| keywords[13].id | https://openalex.org/keywords/simple |
| keywords[13].score | 0.4310082793235779 |
| keywords[13].display_name | Simple (philosophy) |
| keywords[14].id | https://openalex.org/keywords/ideal |
| keywords[14].score | 0.4217422902584076 |
| keywords[14].display_name | Ideal (ethics) |
| keywords[15].id | https://openalex.org/keywords/data-mining |
| keywords[15].score | 0.2284134030342102 |
| keywords[15].display_name | Data mining |
| keywords[16].id | https://openalex.org/keywords/mathematics |
| keywords[16].score | 0.10845485329627991 |
| keywords[16].display_name | Mathematics |
| keywords[17].id | https://openalex.org/keywords/programming-language |
| keywords[17].score | 0.0720197856426239 |
| keywords[17].display_name | Programming language |
| language | en |
| locations[0].id | doi:10.18653/v1/2023.acl-long.128 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://aclanthology.org/2023.acl-long.128.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
| locations[0].landing_page_url | https://doi.org/10.18653/v1/2023.acl-long.128 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101643423 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2064-5407 |
| authorships[0].author.display_name | Jinghui Lu |
| authorships[0].affiliations[0].raw_affiliation_string | SenseTime Research |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jinghui Lu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | SenseTime Research |
| authorships[1].author.id | https://openalex.org/A5102006466 |
| authorships[1].author.orcid | https://orcid.org/0009-0000-4033-8194 |
| authorships[1].author.display_name | Dongsheng Zhu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I24943067 |
| authorships[1].affiliations[0].raw_affiliation_string | Fudan University |
| authorships[1].institutions[0].id | https://openalex.org/I24943067 |
| authorships[1].institutions[0].ror | https://ror.org/013q1eq08 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I24943067 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Fudan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dongsheng Zhu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Fudan University |
| authorships[2].author.id | https://openalex.org/A5067287490 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3207-3899 |
| authorships[2].author.display_name | Weidong Han |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I24943067 |
| authorships[2].affiliations[0].raw_affiliation_string | Fudan University |
| authorships[2].institutions[0].id | https://openalex.org/I24943067 |
| authorships[2].institutions[0].ror | https://ror.org/013q1eq08 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I24943067 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Fudan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Weidong Han |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Fudan University |
| authorships[3].author.id | https://openalex.org/A5100684043 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2993-2023 |
| authorships[3].author.display_name | Rui Zhao |
| authorships[3].affiliations[0].raw_affiliation_string | SenseTime Research |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Rui Zhao |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | SenseTime Research |
| authorships[4].author.id | https://openalex.org/A5084251444 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2518-0274 |
| authorships[4].author.display_name | Brian Mac Namee |
| authorships[4].countries | IE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I100930933 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Computer Science, University College Dublin |
| authorships[4].institutions[0].id | https://openalex.org/I100930933 |
| authorships[4].institutions[0].ror | https://ror.org/05m7pjf47 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I100930933 |
| authorships[4].institutions[0].country_code | IE |
| authorships[4].institutions[0].display_name | University College Dublin |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Brian Mac Namee |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Computer Science, University College Dublin |
| authorships[5].author.id | https://openalex.org/A5100740041 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3232-1912 |
| authorships[5].author.display_name | Fei Tan |
| authorships[5].affiliations[0].raw_affiliation_string | SenseTime Research |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Fei Tan |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | SenseTime Research |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://aclanthology.org/2023.acl-long.128.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | What Makes Pre-trained Language Models Better Zero-shot Learners? |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10181 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9933000206947327 |
| 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 | Natural Language Processing Techniques |
| related_works | https://openalex.org/W2169518243, https://openalex.org/W2252095989, https://openalex.org/W4322096525, https://openalex.org/W2551914602, https://openalex.org/W4281893144, https://openalex.org/W2105076537, https://openalex.org/W2787311093, https://openalex.org/W2921174581, https://openalex.org/W2084531783, https://openalex.org/W2020757772 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 1 |
| best_oa_location.id | doi:10.18653/v1/2023.acl-long.128 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://aclanthology.org/2023.acl-long.128.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
| best_oa_location.landing_page_url | https://doi.org/10.18653/v1/2023.acl-long.128 |
| primary_location.id | doi:10.18653/v1/2023.acl-long.128 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://aclanthology.org/2023.acl-long.128.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
| primary_location.landing_page_url | https://doi.org/10.18653/v1/2023.acl-long.128 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2149741699, https://openalex.org/W4385573504, https://openalex.org/W3172642864, https://openalex.org/W4286769130, https://openalex.org/W4382202624, https://openalex.org/W3122241445, https://openalex.org/W3034775979, https://openalex.org/W3174770825, https://openalex.org/W3099215402, https://openalex.org/W3034999214, https://openalex.org/W4297795751, https://openalex.org/W4205991051, https://openalex.org/W2923014074, https://openalex.org/W4297801719, https://openalex.org/W4206214875, https://openalex.org/W4221140922, https://openalex.org/W3099655892, https://openalex.org/W2965373594, https://openalex.org/W4288089799, https://openalex.org/W3172943453, https://openalex.org/W3166396011, https://openalex.org/W4322718191, https://openalex.org/W2896457183, https://openalex.org/W4292779060, https://openalex.org/W4285247752, https://openalex.org/W4287891464, https://openalex.org/W3197298549, https://openalex.org/W2170240176, https://openalex.org/W3198377975, https://openalex.org/W4362515116, https://openalex.org/W3182414670, https://openalex.org/W2889326414, https://openalex.org/W4285294723, https://openalex.org/W3173777717 |
| referenced_works_count | 34 |
| abstract_inverted_index.a | 11, 24, 32, 47, 82, 107 |
| abstract_inverted_index.We | 64 |
| abstract_inverted_index.be | 70 |
| abstract_inverted_index.in | 5, 31, 57, 94, 106 |
| abstract_inverted_index.is | 27, 42 |
| abstract_inverted_index.no | 39 |
| abstract_inverted_index.of | 36, 76, 91 |
| abstract_inverted_index.on | 10 |
| abstract_inverted_index.to | 18, 72, 102 |
| abstract_inverted_index.we | 45 |
| abstract_inverted_index.and | 79 |
| abstract_inverted_index.any | 115 |
| abstract_inverted_index.can | 69 |
| abstract_inverted_index.for | 2, 52, 87, 114 |
| abstract_inverted_index.not | 28 |
| abstract_inverted_index.our | 99 |
| abstract_inverted_index.set | 13 |
| abstract_inverted_index.the | 20, 74, 89, 112 |
| abstract_inverted_index.yet | 49 |
| abstract_inverted_index.This | 26 |
| abstract_inverted_index.data | 17, 41 |
| abstract_inverted_index.need | 113 |
| abstract_inverted_index.rely | 9 |
| abstract_inverted_index.show | 97 |
| abstract_inverted_index.text | 59 |
| abstract_inverted_index.that | 66, 98 |
| abstract_inverted_index.used | 71 |
| abstract_inverted_index.with | 14 |
| abstract_inverted_index.Thus, | 44 |
| abstract_inverted_index.ideal | 29 |
| abstract_inverted_index.leads | 101 |
| abstract_inverted_index.method | 51, 100 |
| abstract_inverted_index.prompt | 3, 22, 55, 77, 92 |
| abstract_inverted_index.scheme | 85 |
| abstract_inverted_index.select | 19 |
| abstract_inverted_index.simple | 48 |
| abstract_inverted_index.widely | 8 |
| abstract_inverted_index.Current | 0 |
| abstract_inverted_index.because | 30 |
| abstract_inverted_index.develop | 81 |
| abstract_inverted_index.measure | 73 |
| abstract_inverted_index.methods | 1 |
| abstract_inverted_index.propose | 46 |
| abstract_inverted_index.thereby | 80 |
| abstract_inverted_index.advance. | 95 |
| abstract_inverted_index.allowing | 86 |
| abstract_inverted_index.efficacy | 75 |
| abstract_inverted_index.improved | 103 |
| abstract_inverted_index.labelled | 40, 116 |
| abstract_inverted_index.language | 67 |
| abstract_inverted_index.learning | 4 |
| abstract_inverted_index.scenario | 35 |
| abstract_inverted_index.setting, | 110 |
| abstract_inverted_index.template | 23 |
| abstract_inverted_index.Selection | 62 |
| abstract_inverted_index.effective | 50 |
| abstract_inverted_index.examples. | 117 |
| abstract_inverted_index.practical | 37 |
| abstract_inverted_index.realistic | 108 |
| abstract_inverted_index.scenarios | 7 |
| abstract_inverted_index.screening | 53 |
| abstract_inverted_index.templates | 56, 93 |
| abstract_inverted_index.zero-shot | 6, 34, 58, 109 |
| abstract_inverted_index.Perplexity | 61 |
| abstract_inverted_index.available. | 43 |
| abstract_inverted_index.prediction | 104 |
| abstract_inverted_index.real-world | 33 |
| abstract_inverted_index.reasonable | 54 |
| abstract_inverted_index.relevance, | 38 |
| abstract_inverted_index.sufficient | 15 |
| abstract_inverted_index.templates, | 78 |
| abstract_inverted_index.Experiments | 96 |
| abstract_inverted_index.development | 12 |
| abstract_inverted_index.discrepancy | 68 |
| abstract_inverted_index.eliminating | 111 |
| abstract_inverted_index.forecasting | 88 |
| abstract_inverted_index.hypothesize | 65 |
| abstract_inverted_index.performance | 90, 105 |
| abstract_inverted_index.posteriori. | 25 |
| abstract_inverted_index.substantiated | 83 |
| abstract_inverted_index.(Perplection). | 63 |
| abstract_inverted_index.best-performing | 21 |
| abstract_inverted_index.classification: | 60 |
| abstract_inverted_index.human-annotated | 16 |
| abstract_inverted_index.perplexity-based | 84 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.80961352 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |