Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Ezgi Basar

dblp:410/3469 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0002-0879-0150ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › large language model evaluation
syntactic evaluation
0.912025
TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs · EMNLP 2025

Methods — techniques the papers use, named apart from their topics

human acceptability judgments · 0.9
YearPublicationVenuePosition
2025 TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
abstract
We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs).Covering 16 linguistic phenomena with 1000 minimal pairs each, TurBLiMP fills an important gap in linguistic evaluation resources for Turkish.In designing the benchmark, we give extra attention to two properties of Turkish that remain understudied in current syntactic evaluations of LMs, namely word order flexibility and subordination through morphological processes.Our experiments on a wide range of LMs and a newly collected set of human acceptability judgments reveal that even cutting-edge Large LMs still struggle with grammatical phenomena that are not challenging for humans, and may also exhibit different sensitivities to word order and morphological complexity compared to humans.
Ezgi Basar, Francesca Padovani, Jaap Jumelet, Arianna Bisazza
EMNLP1