Papers by Hyunkyung Lee
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources (2023.emnlp-main)
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| Challenge: | Existing dialog inpainting methods generate ConvQA datasets with low contextual relevance due to insufficient learning of question-answer alignment. |
| Approach: | They propose a dialog inpainting method that generates ConvQA datasets from documents . they propose re-ranking tasks and a framework that generate contextually relevant questions . |
| Outcome: | The proposed framework generates ConvQA datasets with high contextual relevance from textual sources. |
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Text-to-SQL aims to convert natural language questions into executable SQL queries. |
| Approach: | They propose a framework that generates and filters self-augmented examples for SQL generation . using self-generated examples, they surpass previous zero-shot and few-shot frameworks . |
| Outcome: | The proposed framework surpasses the previous zero-shot and few-shot frameworks, achieving higher execution accuracy. |
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)
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| Challenge: | Existing methods for conversational query reformulation depend on human annotations. |
| Approach: | They propose a method that reformulates context-dependent conversational queries without relying on human rewrites. |
| Outcome: | The proposed method shows state-of-the-art performance on two widely-used datasets. |
Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation (2025.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. |
| Approach: | They propose a method that rebalances the turn-count distribution of training data to mitigate Format Inertia in medical pre-consultation tasks. |
| Outcome: | The proposed method significantly alleviates Format Inertia in medical pre-consultation tasks. |
Taxonomy of Comprehensive Safety for Clinical Agents (2025.emnlp-industry)
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| Challenge: | Existing methods for ensuring safety in clinical chatbot applications are not suitable for clinical applications. |
| Approach: | They propose a fine-grained taxonomy that integrates safety filtering and tool selection into a single user intent classification step. |
| Outcome: | The proposed taxonomy integrates safety filtering and tool selection into a single user intent classification step. |
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. |
Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions (2024.lrec-main)
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| Challenge: | Existing methods for text similarity measurement focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean. |
| Approach: | They propose a Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. |
| Outcome: | The proposed method outperforms existing methods in Korean and other languages . it identifies which methods preserve semantics and tone while preserving similarity . |