Papers by Takyoung Kim
From Documents to Segments: A Contextual Reformulation for Topic Assignment (2026.findings-acl)
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| Challenge: | Traditional topic modeling treats each document as a single, coherent unit of topic. |
| Approach: | They propose a paradigm that redefines topic assignment at the level of segments . they propose 'segment intrusion task' to extend word intrusion to the span level . |
| Outcome: | The proposed paradigm improves topic purity, interpretability and applicability to multi-theme corpora. |
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications (2023.acl-industry)
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| Challenge: | Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. |
| Approach: | They propose a social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. |
| Outcome: | The proposed dataset reduces social biases by 16.47%p on average for HyperClova (30B and 82B), and GPT-3. |
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Recent advances of large language models (LLMs) have enabled them to provide long and detailed responses by leveraging their parametric knowledge. |
| Approach: | They introduce a dataset of 10K hierarchical sets of information-seeking subqueries that define structured boundaries for outline creation and evaluation in C2 scenarios. |
| Outcome: | The proposed model generates higher-quality outlines that better fulfill diverse user needs when trained with alignment techniques like DPO. |
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration (2023.acl-long)
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Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoungpil Kim, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park, Jung-Woo Ha
| Challenge: | Existing studies focus on coping with social harms that large language models pose . however, discussions on sensitive issues can become toxic even if the users are well-intentioned. |
| Approach: | They propose to use Korean dataset to test whether LLMs can generate offensive content and propagate prejudices. |
| Outcome: | The proposed dataset shows that acceptable response generation improves for HyperCLOVA and GPT-3. |
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking (2022.acl-short)
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| Challenge: | Existing evaluation metrics for dialog state tracking are limited for belief states accumulated as dialog proceeds . relative slot accuracy allows intuitive evaluation by assigning relative scores according to the turn of each dialog . |
| Approach: | They propose to use relative slot accuracy to complement existing evaluation metrics . joint goal accuracy and slot accuracy are used to evaluate accumulated belief states . |
| Outcome: | The proposed metrics focus on "penalizing states that fail to predict," not "reward for well-predicted states" the proposed metrics do not depend on the number of predefined slots, and allow intuitive evaluation . |