Article

How Language Models Understand Honorific Mismatches in Korean

Kangsan Noh1, Sanghoun Song1,, Eunjeong Oh2,
Author Information & Copyright
1Korea University
2SangmyungUniversity
Corresponding author: Associate Professor Department of Linguistics Korea University 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea, E-mail: sanghoun@korea.ac.kr
Corresponding author: Professor Department of English Education Sangmyung University 20 Hongjimun 2-gil, Jongno-gu, Seoul 03016, Korea, E-mail: eoh@smu.ac.kr

* This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5A2A01080365).

ⓒ Copyright 2024 Language Education Institute, Seoul National University. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Oct 30, 2024 ; Revised: Dec 02, 2024 ; Accepted: Dec 03, 2024

Published Online: Dec 31, 2024

ABSTRACT

This study investigates whether language models can process honorific mismatches in Korean, which occur when syntactic agreement in honorification is violated. Two types of mismatches are examined: YN, in which an honorific referent is paired with a non-honorific verb; and NY, in which a non-honorific referent is paired with an honorific verb. Previous studies showed that native speakers consider YN mismatches relatively acceptable but not NY mismatches. To understand the manner by which language models manage such patterns, surprisal-a complexity metric reflecting sentence likelihood-is applied to four Korean models: KR-BERT, KoELECTRA-base, KLUE-RoBERTa-base, and KLUE-RoBERTa-large. A dataset of 3,200 sentences is used to estimate surprisal for NN matches, NY mismatches, YN mismatches, and YY matches. The results show that the models primarily reflect human judgments, i.e., YN mismatches are considered acceptable, whereas NY mismatches are not. However, the models deviated from human-like processing in managing YY matches, where no violations occurred, likely because of the rarity of YY constructions in the training data. This suggests that, whereas the models demonstrate partial success in processing honorifics, they depend on statistical patterns and lack the deeper pragmatic understanding required for full syntactic and contextual competence.

Keywords: functional linguistic competence; honorification; language model; mismatch, surprisal

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