Papers by Seonmin Koo
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation (2022.findings-naacl)
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
| Challenge: | Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning. |
| Approach: | They propose a Korean text-generation dataset for Korean generative commonsense reasoning and language model evaluation using a semi-automatic dataset construction approach. |
| Outcome: | The proposed dataset is available at http://aihub.or.kr/opendata/korea-university. |
Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation (2024.lrec-main)
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Sugyeong Eo, Jungwoo Lim, Chanjun Park, DaHyun Jung, Seonmin Koo, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
| Challenge: | Recent machine translation systems overcome language barriers for a wide range of users, yet they carry the risk of catastrophic meaning deviations. |
| Approach: | They introduce a culture-aware "Politeness" type for detecting critical translation errors . they also provide multiclass labels for critical error detection and critical error type classification . |
| Outcome: | Empirical results show that the proposed method outperforms baselines in both tasks. |
LimaCost: Data Valuation for Instruction Tuning of Large Language Models (2025.findings-emnlp)
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| Challenge: | Instruction tuning is an effective approach for aligning large language models with human intentions. |
| Approach: | They propose a data quality measure that exhibits a strong correlation with model performance. |
| Outcome: | The proposed measure exhibits a strong correlation with model performance. |
Where am I? Large Language Models Wandering between Semantics and Structures in Long Contexts (2024.emnlp-main)
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| Challenge: | Existing evaluations of the open-domain question answering task focus solely on whether the model provides the correct answer. |
| Approach: | They propose to examine the phenomenon of discrepancies in abilities across two distinct tasks—QA and evidence selection—when performed simultaneously. |
| Outcome: | The proposed framework and resources examines the ability of large language models to perform two distinct tasks simultaneously, from the perspective of task alignment. |
Semantic Inversion, Identical Replies: Revisiting Negation Blindness in Large Language Models (2025.emnlp-main)
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| Challenge: | Negation is a common occurrence in the real world and is essential for logical reasoning as it helps understand the opposite or absence of a statement. |
| Approach: | They propose a verification framework that includes task design and measurement methods to verify this phenomenon negation blindness on the query. |
| Outcome: | The proposed framework can be used to verify the model fails to capture semantic contradictions in negated queries despite its accurate understanding of knowledge about positive queries. |
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing (2023.emnlp-main)
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| Challenge: | Conventional evaluation metrics for automatic speech recognition systems produce a singular aggregate score, which is insufficient for understanding specific system vulnerabilities. |
| Approach: | They propose to introduce the Korean Error Explainable Benchmark Dataset for ASR and Post-processing (KEBAP) this method enables a more balanced assessment encompassing speech recognition accuracy and user readability. |
| Outcome: | The proposed method enables a more balanced assessment encompassing speech recognition accuracy and user readability. |
PANDA: Persona Attributes Navigation for Detecting and Alleviating Overuse Problem in Large Language Models (2024.emnlp-main)
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| Challenge: | In persona-grounded dialogue, it is required to respond fluently and ground attributes according to the current conversation topic properly. |
| Approach: | They propose a framework to quantify the persona overuse problem of LLMs by establishing clear standards and verifying various LLM based on them. |
| Outcome: | The proposed framework aims to quantify the persona overuse problem of LLMs by establishing clear standards and verifying various LLM based on them. |
HAWK: Highlighting Entity-aware Knowledge for Alleviating Information Sparsity in Long Contexts (2025.findings-emnlp)
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| Challenge: | a problem of information sparsity in QA tasks is causing fragmentation of textual data . highlighting entity-AWare Knowledge (HAWK) framework can be used to address this problem . |
| Approach: | a framework is proposed to highlight key information in a context and structuralize it in an entity-aware manner. |
| Outcome: | a proposed framework improves QA tasks with long contexts by highlighting key information in a context . the framework achieves a 27.6-point F1 score increase and an average win rate of 76.75% . |
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that the performance of large language models is insufficient for non-English data, such as Korean. |
| Approach: | They propose a framework that integrates evidential information from external sources into the prompt for the Korean GEC task. |
| Outcome: | The proposed framework extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases. |
I Know, but I Don’t Know! How Persona Conflict Undermines Instruction Adherence in Large Language Models (2026.findings-eacl)
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| Challenge: | Existing studies on persona-grounded dialogue assume idealized scenarios where persona and user utterances are fully aligned. |
| Approach: | They propose a taxonomy that categorizes model behaviors into three response types . they propose sycophantic, adherent, and wavering responses as response types. |
| Outcome: | The proposed framework categorizes model behaviors into three response types and develops a measurement schema grounded in this taxonomy. |