Papers by Jongwon Lee
KOLD: Korean Offensive Language Dataset (2022.emnlp-main)
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| Challenge: | Recent directions for offensive language detection focus on English and do not transfer well to other languages because of cultural and linguistic differences. |
| Approach: | They present a Korean offensive language dataset annotated with offensive language comments . they use the comments as training data for Korean BERT and RoBERTa models . |
| Outcome: | The proposed model improves offensiveness detection, target classification, and span detection while having room for improvement for target group classification and span prediction. |
You Only Need One Model for Open-domain Question Answering (2022.emnlp-main)
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| Challenge: | Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training. |
| Approach: | They propose to use a single question answering model trained end-to-end to retrieve external knowledge and rerank passages with a separate reranked model. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores on the Natural Questions and TriviaQA open datasets. |
FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation (2026.findings-eacl)
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| Challenge: | Existing evaluation frameworks lack systematic methods to identify weaknesses in LLMs . Existing methods to evaluate LLM responses to sensitive topics are lacking . |
| Approach: | They propose a FINE-grained response evaluation taxonomy for sensitive topics that breaks down helpfulness and harmlessness into errors across three main categories: Content, Logic, and Appropriateness. |
| Outcome: | The proposed model outperforms refinement without guidance on Korean-sensitive questions . FINEST significantly improves the model responses across all three categories . |
Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines (2026.eacl-industry)
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Jean Seo, Gibaeg Kim, Kihun Shin, Seungseop Lim, Hyunkyung Lee, Wooseok Han, Jongwon Lee, Eunho Yang
| Challenge: | EPAG is a benchmark dataset and evaluation pipeline for pre-consultation of large language models. |
| Approach: | They propose a benchmark dataset and framework for evaluating pre-consultation ability of LLMs using diagnostic guidelines. |
| Outcome: | The proposed framework outperforms frontier LLMs in pre-consultation. |