Papers by Jinsung Kim
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. |
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. |
Debiasing Online Preference Learning via Preference Feature Preservation (2025.findings-acl)
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| Challenge: | Recent preference learning frameworks for large language models (LLMs) simplify human preferences with binary pairwise comparisons and scalar rewards. |
| Approach: | They propose a preference feature preservation framework that preserves the distribution of human preference features and maps them throughout the online preference learning process. |
| Outcome: | The proposed framework maintains the distribution of human preference features and utilizing such rich signals throughout the online preference learning process. |
You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022.findings-emnlp)
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
| Challenge: | Existing models that ground knowledge and persona at the same time are limited, leading to hallucination and a passive way of using personas. |
| Approach: | They propose a conversational agent that grounds external knowledge and persona simultaneously and a retrieval augmented generation model that generates utterances with lesser hallucination and more engagingness. |
| Outcome: | The proposed agent generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. |
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks (2023.findings-acl)
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Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, Yuna Hur, Jeongwook Kim, SongEun Lee, Changwoo Chun, Sungsoo Park, Heuiseok Lim
| Challenge: | Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. |
| Approach: | They propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by significant margins, achieving improved diversity and quality. |
KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks (2022.coling-1)
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| Challenge: | Existing corpus for entity-related tasks is limited in terms of application and cannot be used for entity recognition. |
| Approach: | They propose to use a Korean cultural heritage corpus for the typical entity-related tasks named entity recognition (NER), relation extraction (RE) and entity typing (ET) . |
| Outcome: | The proposed corpus makes it more useful in terms of cultural heritage and provides practical insights in terms linguistic analysis. |
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. |
Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023.findings-emnlp)
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| Challenge: | a previous study suggested that human dialogue systems ground persona and knowledge but they require incomplete candidate sets. |
| Approach: | They propose an adaptive dialogue agent that uses persona and knowledge without candidate sets . their model generates consistent and relevant persona descriptions and identifies relevant knowledge . |
| Outcome: | The proposed model outperforms baselines that ground persona and knowledge candidates even with fragmentary information. |
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% . |
GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies utilize pre-trained language models with extensive features to supplement the low information density of the dialogue by multiple speakers. |
| Approach: | They propose a dialogue-based relation extraction task that leverages pre-trained language models to capture relational semantic clues of a given dialogue using an argument-aware prompt marker strategy and a relational clue detection task. |
| Outcome: | The proposed model achieves state-of-the-art on a DialogRE dataset even though it only leverages pre-trained language models without adding any extra layers. |
Explore the Way: Exploring Reasoning Path by Bridging Entities for Effective Cross-Document Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies focus on capturing implicit relations between entities, but humans use explicit information chains to find the relations between two entities. |
| Approach: | They propose a cross-document relation extraction task that captures the relation between two entities mentioned in different documents. |
| Outcome: | The proposed model outperforms baseline models in the CodRED task. |
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. |