LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models Article Swipe
Xiaohao Yang
,
He Zhao
,
Dinh Phung
,
Wray Buntine
,
Lan Du
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1162/tacl_a_00744
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1162/tacl_a_00744
Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are either less comparable across different models (e.g., perplexity) or focus on only one specific aspect of a model (e.g., topic quality or document representation quality) at a time, which is insufficient to reflect the overall model performance. In this paper, we propose WALM (Word Agreement with Language Model), a new evaluation method for topic modeling that considers the semantic quality of document representations and topics in a joint manner, leveraging the power of Large Language Models (LLMs). With extensive experiments involving different types of topic models, WALM is shown to align with human judgment and can serve as a complementary evaluation method to the existing ones, bringing a new perspective to topic modeling. Our software package is available at https://github.com/Xiaohao-Yang/Topic_Model_Evaluation.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1162/tacl_a_00744
- https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 91
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409872524
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409872524Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1162/tacl_a_00744Digital Object Identifier
- Title
-
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Xiaohao Yang, He Zhao, Dinh Phung, Wray Buntine, Lan DuList of authors in order
- Landing page
-
https://doi.org/10.1162/tacl_a_00744Publisher landing page
- PDF URL
-
https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdfDirect OA link when available
- Concepts
-
Computer science, Reading (process), Language model, Natural language processing, Artificial intelligence, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4Per-year citation counts (last 5 years)
- References (count)
-
91Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4409872524 |
|---|---|
| doi | https://doi.org/10.1162/tacl_a_00744 |
| ids.doi | https://doi.org/10.1162/tacl_a_00744 |
| ids.openalex | https://openalex.org/W4409872524 |
| fwci | 19.27898059 |
| type | article |
| title | LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | 375 |
| biblio.first_page | 357 |
| topics[0].id | https://openalex.org/T13083 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9977999925613403 |
| 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 | Advanced Text Analysis Techniques |
| topics[1].id | https://openalex.org/T13910 |
| topics[1].field.id | https://openalex.org/fields/33 |
| topics[1].field.display_name | Social Sciences |
| topics[1].score | 0.9800000190734863 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3300 |
| topics[1].subfield.display_name | General Social Sciences |
| topics[1].display_name | Computational and Text Analysis Methods |
| topics[2].id | https://openalex.org/T12205 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9230999946594238 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1711 |
| topics[2].subfield.display_name | Signal Processing |
| topics[2].display_name | Time Series Analysis and Forecasting |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8161014318466187 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C554936623 |
| concepts[1].level | 2 |
| concepts[1].score | 0.619273841381073 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q199657 |
| concepts[1].display_name | Reading (process) |
| concepts[2].id | https://openalex.org/C137293760 |
| concepts[2].level | 2 |
| concepts[2].score | 0.593981921672821 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3621696 |
| concepts[2].display_name | Language model |
| concepts[3].id | https://openalex.org/C204321447 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5732881426811218 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[3].display_name | Natural language processing |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5045062303543091 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C41895202 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3251546621322632 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[5].display_name | Linguistics |
| concepts[6].id | https://openalex.org/C138885662 |
| concepts[6].level | 0 |
| concepts[6].score | 0.0 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[6].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8161014318466187 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/reading |
| keywords[1].score | 0.619273841381073 |
| keywords[1].display_name | Reading (process) |
| keywords[2].id | https://openalex.org/keywords/language-model |
| keywords[2].score | 0.593981921672821 |
| keywords[2].display_name | Language model |
| keywords[3].id | https://openalex.org/keywords/natural-language-processing |
| keywords[3].score | 0.5732881426811218 |
| keywords[3].display_name | Natural language processing |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5045062303543091 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/linguistics |
| keywords[5].score | 0.3251546621322632 |
| keywords[5].display_name | Linguistics |
| language | en |
| locations[0].id | doi:10.1162/tacl_a_00744 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2729999759 |
| locations[0].source.issn | 2307-387X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2307-387X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Transactions of the Association for Computational Linguistics |
| locations[0].source.host_organization | https://openalex.org/P4310320244 |
| locations[0].source.host_organization_name | Association for Computational Linguistics |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320244 |
| locations[0].source.host_organization_lineage_names | Association for Computational Linguistics |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-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 | Transactions of the Association for Computational Linguistics |
| locations[0].landing_page_url | https://doi.org/10.1162/tacl_a_00744 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5011972821 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6136-3575 |
| authorships[0].author.display_name | Xiaohao Yang |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I56590836 |
| authorships[0].affiliations[0].raw_affiliation_string | Monash University, Melbourne, Australia. [email protected] |
| authorships[0].institutions[0].id | https://openalex.org/I56590836 |
| authorships[0].institutions[0].ror | https://ror.org/02bfwt286 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I56590836 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Monash University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xiaohao Yang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Monash University, Melbourne, Australia. [email protected] |
| authorships[1].author.id | https://openalex.org/A5101618917 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2146-5563 |
| authorships[1].author.display_name | He Zhao |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1292875679, https://openalex.org/I42894916 |
| authorships[1].affiliations[0].raw_affiliation_string | CSIRO’s Data61, Sydney, Australia. [email protected] |
| authorships[1].institutions[0].id | https://openalex.org/I1292875679 |
| authorships[1].institutions[0].ror | https://ror.org/03qn8fb07 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I1292875679, https://openalex.org/I2801453606, https://openalex.org/I4387156119 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | Commonwealth Scientific and Industrial Research Organisation |
| authorships[1].institutions[1].id | https://openalex.org/I42894916 |
| authorships[1].institutions[1].ror | https://ror.org/03q397159 |
| authorships[1].institutions[1].type | other |
| authorships[1].institutions[1].lineage | https://openalex.org/I1292875679, https://openalex.org/I2801453606, https://openalex.org/I42894916, https://openalex.org/I4387156119 |
| authorships[1].institutions[1].country_code | AU |
| authorships[1].institutions[1].display_name | Data61 |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | He Zhao |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | CSIRO’s Data61, Sydney, Australia. [email protected] |
| authorships[2].author.id | https://openalex.org/A5036447132 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9977-8247 |
| authorships[2].author.display_name | Dinh Phung |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I56590836 |
| authorships[2].affiliations[0].raw_affiliation_string | Monash University, Melbourne, Australia. [email protected] |
| authorships[2].institutions[0].id | https://openalex.org/I56590836 |
| authorships[2].institutions[0].ror | https://ror.org/02bfwt286 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I56590836 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | Monash University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dinh Phung |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Monash University, Melbourne, Australia. [email protected] |
| authorships[3].author.id | https://openalex.org/A5005792924 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9292-1015 |
| authorships[3].author.display_name | Wray Buntine |
| authorships[3].countries | VN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210142044 |
| authorships[3].affiliations[0].raw_affiliation_string | VinUniversity, Hanoi, Vietnam. [email protected] |
| authorships[3].institutions[0].id | https://openalex.org/I4210142044 |
| authorships[3].institutions[0].ror | https://ror.org/052dmdr17 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210142044 |
| authorships[3].institutions[0].country_code | VN |
| authorships[3].institutions[0].display_name | VinUniversity |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Wray Buntine |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | VinUniversity, Hanoi, Vietnam. [email protected] |
| authorships[4].author.id | https://openalex.org/A5100716826 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Lan Du |
| authorships[4].countries | AU |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I56590836 |
| authorships[4].affiliations[0].raw_affiliation_string | Monash University, Melbourne, Australia. [email protected] |
| authorships[4].institutions[0].id | https://openalex.org/I56590836 |
| authorships[4].institutions[0].ror | https://ror.org/02bfwt286 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I56590836 |
| authorships[4].institutions[0].country_code | AU |
| authorships[4].institutions[0].display_name | Monash University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Lan Du |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Monash University, Melbourne, Australia. [email protected] |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13083 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9977999925613403 |
| 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 | Advanced Text Analysis Techniques |
| related_works | https://openalex.org/W2082438799, https://openalex.org/W1966986837, https://openalex.org/W2360138227, https://openalex.org/W4365808155, https://openalex.org/W1838455177, https://openalex.org/W1489909378, https://openalex.org/W2392697679, https://openalex.org/W2385990477, https://openalex.org/W2331964906, https://openalex.org/W3204019825 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1162/tacl_a_00744 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2729999759 |
| best_oa_location.source.issn | 2307-387X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2307-387X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Transactions of the Association for Computational Linguistics |
| best_oa_location.source.host_organization | https://openalex.org/P4310320244 |
| best_oa_location.source.host_organization_name | Association for Computational Linguistics |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320244 |
| best_oa_location.source.host_organization_lineage_names | Association for Computational Linguistics |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-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 | Transactions of the Association for Computational Linguistics |
| best_oa_location.landing_page_url | https://doi.org/10.1162/tacl_a_00744 |
| primary_location.id | doi:10.1162/tacl_a_00744 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2729999759 |
| primary_location.source.issn | 2307-387X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2307-387X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Transactions of the Association for Computational Linguistics |
| primary_location.source.host_organization | https://openalex.org/P4310320244 |
| primary_location.source.host_organization_name | Association for Computational Linguistics |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320244 |
| primary_location.source.host_organization_lineage_names | Association for Computational Linguistics |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00744/2514623/tacl_a_00744.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-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 | Transactions of the Association for Computational Linguistics |
| primary_location.landing_page_url | https://doi.org/10.1162/tacl_a_00744 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W6865118152, https://openalex.org/W102708294, https://openalex.org/W4403577816, https://openalex.org/W2625714162, https://openalex.org/W2892379661, https://openalex.org/W2150286230, https://openalex.org/W6639619044, https://openalex.org/W6778883912, https://openalex.org/W6809667296, https://openalex.org/W1559926086, https://openalex.org/W2955792000, https://openalex.org/W6683333316, https://openalex.org/W6810081322, https://openalex.org/W3045464143, https://openalex.org/W3166574703, https://openalex.org/W6795007937, https://openalex.org/W6683566454, https://openalex.org/W3167308647, https://openalex.org/W6810311456, https://openalex.org/W6802527980, https://openalex.org/W2167329753, https://openalex.org/W4206776287, https://openalex.org/W6858023062, https://openalex.org/W2222512263, https://openalex.org/W1493526108, https://openalex.org/W6683240801, https://openalex.org/W2250533720, https://openalex.org/W6851866189, https://openalex.org/W2522581443, https://openalex.org/W6769849856, https://openalex.org/W4391094508, https://openalex.org/W6636001973, https://openalex.org/W6738975087, https://openalex.org/W6636510571, https://openalex.org/W2081580037, https://openalex.org/W6679482899, https://openalex.org/W6682044806, https://openalex.org/W6803501903, https://openalex.org/W6798997931, https://openalex.org/W2461271816, https://openalex.org/W2005564522, https://openalex.org/W2250539671, https://openalex.org/W4401043789, https://openalex.org/W6852591161, https://openalex.org/W2890363010, https://openalex.org/W2038043464, https://openalex.org/W3100651519, https://openalex.org/W6734688555, https://openalex.org/W4389520774, https://openalex.org/W4386120650, https://openalex.org/W6811129797, https://openalex.org/W6850625674, https://openalex.org/W6854866820, https://openalex.org/W4392182252, https://openalex.org/W6850158284, https://openalex.org/W2144100511, https://openalex.org/W6810378106, https://openalex.org/W4389519799, https://openalex.org/W6855571639, https://openalex.org/W6870500042, https://openalex.org/W2155188720, https://openalex.org/W4391494845, https://openalex.org/W3191804854, https://openalex.org/W2804385163, https://openalex.org/W3134283136, https://openalex.org/W3092956426, https://openalex.org/W4321524684, https://openalex.org/W6727968406, https://openalex.org/W4322718191, https://openalex.org/W1614298861, https://openalex.org/W3099531031, https://openalex.org/W2157006255, https://openalex.org/W4286895139, https://openalex.org/W4287245321, https://openalex.org/W2529448042, https://openalex.org/W4292779060, https://openalex.org/W3128419247, https://openalex.org/W4367623968, https://openalex.org/W4221142221, https://openalex.org/W4231510805, https://openalex.org/W2613000335, https://openalex.org/W4385261749, https://openalex.org/W4392617597, https://openalex.org/W4226399820, https://openalex.org/W4224308101, https://openalex.org/W4384918448, https://openalex.org/W4386566590, https://openalex.org/W4288027461, https://openalex.org/W1554540371, https://openalex.org/W2159426623, https://openalex.org/W4395474395 |
| referenced_works_count | 91 |
| abstract_inverted_index.a | 5, 17, 42, 52, 74, 92, 124, 133 |
| abstract_inverted_index.In | 63 |
| abstract_inverted_index.as | 123 |
| abstract_inverted_index.at | 51, 144 |
| abstract_inverted_index.in | 91 |
| abstract_inverted_index.is | 55, 113, 142 |
| abstract_inverted_index.of | 16, 41, 86, 98, 109 |
| abstract_inverted_index.on | 36 |
| abstract_inverted_index.or | 34, 47 |
| abstract_inverted_index.to | 57, 115, 128, 136 |
| abstract_inverted_index.we | 66 |
| abstract_inverted_index.Our | 139 |
| abstract_inverted_index.and | 89, 120 |
| abstract_inverted_index.are | 25 |
| abstract_inverted_index.can | 121 |
| abstract_inverted_index.for | 9, 78 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.new | 75, 134 |
| abstract_inverted_index.one | 38 |
| abstract_inverted_index.the | 59, 83, 96, 129 |
| abstract_inverted_index.WALM | 68, 112 |
| abstract_inverted_index.With | 103 |
| abstract_inverted_index.been | 4 |
| abstract_inverted_index.less | 27 |
| abstract_inverted_index.only | 37 |
| abstract_inverted_index.text | 11 |
| abstract_inverted_index.that | 81 |
| abstract_inverted_index.this | 64 |
| abstract_inverted_index.tool | 8 |
| abstract_inverted_index.used | 7 |
| abstract_inverted_index.with | 71, 117 |
| abstract_inverted_index.(Word | 69 |
| abstract_inverted_index.Large | 99 |
| abstract_inverted_index.Topic | 1 |
| abstract_inverted_index.align | 116 |
| abstract_inverted_index.focus | 35 |
| abstract_inverted_index.human | 118 |
| abstract_inverted_index.joint | 93 |
| abstract_inverted_index.model | 19, 43, 61 |
| abstract_inverted_index.ones, | 131 |
| abstract_inverted_index.power | 97 |
| abstract_inverted_index.serve | 122 |
| abstract_inverted_index.shown | 114 |
| abstract_inverted_index.time, | 53 |
| abstract_inverted_index.topic | 18, 45, 79, 110, 137 |
| abstract_inverted_index.types | 108 |
| abstract_inverted_index.which | 54 |
| abstract_inverted_index.(e.g., | 32, 44 |
| abstract_inverted_index.Models | 101 |
| abstract_inverted_index.across | 29 |
| abstract_inverted_index.aspect | 40 |
| abstract_inverted_index.either | 26 |
| abstract_inverted_index.method | 77, 127 |
| abstract_inverted_index.models | 31 |
| abstract_inverted_index.paper, | 65 |
| abstract_inverted_index.remain | 20 |
| abstract_inverted_index.topics | 90 |
| abstract_inverted_index.widely | 6 |
| abstract_inverted_index.(LLMs). | 102 |
| abstract_inverted_index.Model), | 73 |
| abstract_inverted_index.manner, | 94 |
| abstract_inverted_index.methods | 24 |
| abstract_inverted_index.models, | 111 |
| abstract_inverted_index.overall | 60 |
| abstract_inverted_index.package | 141 |
| abstract_inverted_index.propose | 67 |
| abstract_inverted_index.quality | 46, 85 |
| abstract_inverted_index.reflect | 58 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Existing | 22 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.Language | 72, 100 |
| abstract_inverted_index.bringing | 132 |
| abstract_inverted_index.document | 48, 87 |
| abstract_inverted_index.existing | 130 |
| abstract_inverted_index.judgment | 119 |
| abstract_inverted_index.modeling | 2, 80 |
| abstract_inverted_index.quality) | 50 |
| abstract_inverted_index.semantic | 84 |
| abstract_inverted_index.software | 140 |
| abstract_inverted_index.specific | 39 |
| abstract_inverted_index.Agreement | 70 |
| abstract_inverted_index.analysis. | 12 |
| abstract_inverted_index.available | 143 |
| abstract_inverted_index.considers | 82 |
| abstract_inverted_index.different | 30, 107 |
| abstract_inverted_index.extensive | 104 |
| abstract_inverted_index.involving | 106 |
| abstract_inverted_index.modeling. | 138 |
| abstract_inverted_index.comparable | 28 |
| abstract_inverted_index.evaluation | 23, 76, 126 |
| abstract_inverted_index.leveraging | 95 |
| abstract_inverted_index.evaluations | 15 |
| abstract_inverted_index.experiments | 105 |
| abstract_inverted_index.perplexity) | 33 |
| abstract_inverted_index.perspective | 135 |
| abstract_inverted_index.challenging. | 21 |
| abstract_inverted_index.insufficient | 56 |
| abstract_inverted_index.performance. | 62 |
| abstract_inverted_index.unsupervised | 10 |
| abstract_inverted_index.complementary | 125 |
| abstract_inverted_index.comprehensive | 14 |
| abstract_inverted_index.representation | 49 |
| abstract_inverted_index.representations | 88 |
| abstract_inverted_index.https://github.com/Xiaohao-Yang/Topic_Model_Evaluation. | 145 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.98989281 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |