Papers by Hyunkyung Bae
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models (2025.findings-naacl)
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| Challenge: | Knowledge Distillation (KD) has emerged as a popular method for compressing large language models due to high inference costs and memory requirements. |
| Approach: | They propose a method that integrates the teacher model during the student's sequence generation to reduce misguidance from the teacher. |
| Outcome: | Experiments on three model families and five instruction-following datasets show that SWITCH surpasses traditional methods, especially in the generation of long sequential data. |
Reducing Peak Memory Usage for Modern Multimodal Large Language Model Pipelines (2026.findings-acl)
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| Challenge: | Existing methods to reduce memory usage of multimodal large language models rely on storing large numbers of vision tokens in the key–value cache . however, such compression is typically only applied after all inputs are processed, resulting in high peak memory usage during the prefill stage. |
| Approach: | They propose a sequential input-compression mechanism that enforces a fixed memory budget by performing structure-aware key–value cache compression during the prefill stage. |
| Outcome: | The proposed approach reduces peak memory usage while maintaining generative performance with only minimal degradation, enabling more practical and memory-efficient multimodal inference. |
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs (2024.emnlp-main)
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| Challenge: | Existing methods to manage topic shifts within on-topic dialogues are limited in their ability to generate training datasets. |
| Approach: | They propose a data generation framework that automatically generates conversational question-answering datasets with natural topic transitions by leveraging relationships between entities in a knowledge graph. |
| Outcome: | The proposed framework generates conversational question-answering datasets with natural topic transitions and proves its effectiveness in generating dialogues with topic shifts. |
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. |
LLMs can be easily Confused by Instructional Distractions (2025.acl-long)
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| Challenge: | Large language models (LLMs) show exceptional skill in instruction following tasks, but can become vulnerable when they are required to disregard instructions. |
| Approach: | They propose a benchmark to assess LLMs' performance under instructional distraction. |
| Outcome: | The proposed benchmark categorizes real-world instances of instructional distraction and evaluates LLMs across four instruction tasks: proofreading, rewriting, translation, and style transfer—alongside five input tasks: reasoning, code generation, mathematical reasoning, bias detection, and question answering. |
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 . |
Injecting Comparison Skills in Task-Oriented Dialogue Systems for Database Search Results Disambiguation (2023.findings-acl)
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| Challenge: | Existing task-oriented dialogue systems handle ambiguity in database search results by randomly selecting one or few results and presenting their names to the user. |
| Approach: | They propose a task that compares properties of multiple database search results . they use a dataset to collect high-quality dialogue data and an augmented version of the SGD dataset . |
| Outcome: | The proposed task compares properties of two entities in a trade-off form based on user preferences . the proposed dataset and code will be publicized . |