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Taehee Jeon

dblp:409/8164 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 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
Knowledge representation and reasoning · 77% Information extraction and text analysis · 23%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge management › knowledge evaluation
linguistic knowledge evaluation
0.912025
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean · ACL (1) 2025

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

zero-shot evaluation · 0.9multiple-choice QA · 0.9
YearPublicationVenuePosition
2025 Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean
abstract
We introduce the Korean Grammar Evaluation BenchMark (KoGEM), designed to assess the linguistic competence of LLMs and humans in Korean.KoGEM consists of 1.5k multiplechoice QA pairs covering five main categories and 16 subcategories.The zero-shot evaluation of 27 LLMs of various sizes and types reveals that while LLMs perform remarkably well on straightforward tasks requiring primarily definitional knowledge, they struggle with tasks that demand the integration of realworld experiential knowledge, such as phonological rules and pronunciation.Furthermore, our in-depth analysis suggests that incorporating such experiential knowledge could enhance the linguistic competence of LLMs.With Ko-GEM, we not only highlight the limitations of current LLMs in linguistic competence but also uncover hidden facets of LLMs in linguistic competence, paving the way for enhancing comprehensive language understanding.Our code and dataset are available at
Sung-Ho Kim 0010, Nayeon Kim 0002, Taehee Jeon, SangKeun Lee 0001
ACL (1)3