Papers by Donghyun Kim
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA (2026.eacl-long)
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| Challenge: | Recent studies leverage large language models (LLMs) in LVQA benchmarks, achieving exceptional performance while relying on vision language models to convert all visual content into natural language. |
| Approach: | They propose a modular and training-free framework that leverages large language models to generate a small subset of informative frames tailored to each question. |
| Outcome: | The proposed framework achieves state-of-the-art performance among similar models across four benchmark LVQA datasets: EgoSchema, NExT-QA, IntentQA, VideoMME. |
Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation (2026.findings-acl)
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| Challenge: | Existing data selection methods struggle to distinguish learnable samples under contextual shifts. |
| Approach: | They propose a framework agnostic to underlying large language model-based conversational recommender systems (CRSs) that captures user preferences through free-form conversations and generates contextually relevant recommendations. |
| Outcome: | The proposed framework outperforms baselines on three CRS benchmarks with real-world temporal splits. |
MATE: Meet At The Embedding - Connecting Images with Long Texts (2024.findings-emnlp)
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| Challenge: | Recent advances in Vision Language Models (VLMs) focus on aligning images with short descriptive captions. |
| Approach: | They propose a method that combines VLMs with Large Language Models to efficiently align images with long texts without additional text pairs. |
| Outcome: | The proposed method bridges the gap between VLM and LLM without additional image-long text pairs. |
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning (2026.eacl-long)
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| Challenge: | Recent advances in reinforcement learning with verifiable rewards (RLVR) show that large language models enhance their reasoning abilities when trained with veriable signals. |
| Approach: | They propose a method for a problem-aware filtering system that maximizes learning efficiency by selecting tasks of intermediate difficulty. |
| Outcome: | The proposed model improves when trained with verifiable rewards, but training efficiency is bottleneck . the proposed model achieves +12% gains in less than half the training steps of standard GRPO . |
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung
| Challenge: | GPT-3 has been used to train large-scale language models on hundreds of billion scale data. |
| Approach: | They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models. |
| Outcome: | The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean. |
Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023.eacl-main)
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
| Approach: | They propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. |
| Outcome: | The proposed framework improves not only the diversity but also the consistency of persona-based responses on the Persona-Chat dataset. |
Large Language Models as Realistic Microservice Trace Generators (2025.emnlp-main)
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| Challenge: | Obtaining real-world traces is difficult due to limited public data availability and the difficulty of collecting them at large scale from diverse environments. |
| Approach: | They propose to train a large language model to generate microservice call graphs using a recursive approach to capture hierarchical structures and implicit constraints in such traces. |
| Outcome: | The proposed method outperforms existing methods in accuracy and validity. |
Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023.eacl-main)
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| Challenge: | Existing approaches to improve the quality of persona-grounded dialogues are limited to a few informative words. |
| Approach: | They propose a concept-based persona expansion framework that takes the original persona as input and generates expanded personas that contain conceptually rich content. |
| Outcome: | The proposed framework improves the quality of persona-grounded dialogue responses in diversity and richness. |
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues (N19-1)
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| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
| Approach: | They develop a pre-trained conversation model that learns to classify client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. |
| Outcome: | The proposed model outperforms state-of-the-art comparison models and shows expected linguistic patterns for each category. |
Keep Me Updated! Memory Management in Long-term Conversations (2022.findings-emnlp)
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Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee, Sungdong Kim, Yuin Jeong, Hyeri Kim, Sang-Woo Lee, Woomyoung Park, Nako Sung
| Challenge: | Existing studies do not deal with cases where memorized information is outdated, which may cause confusion in later conversations. |
| Approach: | They propose a task where bots keep track of and bring up the latest information about users while conversing through multiple sessions. |
| Outcome: | The proposed method outperforms baselines that leave the stored memory unchanged in terms of engagingness and humanness, and a larger performance gap in the later sessions. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)
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Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, Jinyoung Yeo
| Challenge: | Recent research explores multi-agent systems where agents collaborate toward shared goals to handle complex tasks. |
| Approach: | They propose a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints. |
| Outcome: | The proposed benchmark shows that privacy constraints degrade collaboration performance and make outcomes depend more on the initiating agent than the partner. |
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
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| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
| Approach: | They propose a framework for imposing roles on open-domain dialogue systems . they use few-shot learning to build a Korean dialogue dataset from scratch . |
| Outcome: | The proposed framework meets role specifications while maintaining conversational abilities. |
Aligning Large Language Models through Synthetic Feedback (2023.emnlp-main)
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| Challenge: | Currently, alignment learning requires significant human demonstrations and feedback from proprietary LLMs such as ChatGPT. |
| Approach: | They propose a framework that uses synthetic feedback to align large language models to human values without extensive human annotations and proprietary LLMs. |
| Outcome: | The proposed model outperforms open-source models on human-annotated demonstrations in alignment benchmarks. |
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)
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Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella
| Challenge: | Existing techniques like distillation and pruning are not efficient for large language models. |
| Approach: | They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately. |
| Outcome: | The proposed framework improves system throughput while minimizing performance degradation. |
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances (2022.naacl-main)
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| Challenge: | Existing approaches for recognizing feature transitions between utterances extract features for the context at the coarse-grained level. |
| Approach: | They propose a method to recognize feature transitions between utterances that helps understand dialogue flow . they propose empathetic response generation strategy to focus on emotion and keywords related to appropriate features when generating responses. |
| Outcome: | The proposed approach outperforms baseline approaches and improves on multi-turn dialogues. |