Papers by Sungwoong Kim
EgoSpeak: Learning When to Speak for Egocentric Conversational Agents in the Wild (2025.findings-naacl)
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Junhyeok Kim, Min Soo Kim, Jiwan Chung, Jungbin Cho, Jisoo Kim, Sungwoong Kim, Gyeongbo Sim, Youngjae Yu
| Challenge: | EgoSpeak predicts when an agent should begin speaking based on egocentric streaming video. |
| Approach: | They propose a framework for real-time speech initiation prediction in egocentric streaming video by modeling the conversation from the camera wearer's first-person perspective. |
| Outcome: | The proposed framework outperforms random and silence-based baselines in real time and highlights the importance of multimodal input and context length in effectively deciding when to speak. |
Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making (2025.emnlp-main)
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Yejin Son, Minseo Kim, Sungwoong Kim, Seungju Han, Jian Kim, Dongju Jang, Youngjae Yu, Chan Young Park
| Challenge: | Existing safety evaluations rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. |
| Approach: | They propose a framework for systematically evaluating the physical safety of LLMs in embodied decision making. |
| Outcome: | The proposed framework assesses the physical safety of LLMs in embodied decision making. |
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback (2024.findings-acl)
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Eunseop Yoon, Hee Suk Yoon, SooHwan Eom, Gunsoo Han, Daniel Nam, Daejin Jo, Kyoung-Woon On, Mark Hasegawa-Johnson, Sungwoong Kim, Chang Yoo
| Challenge: | Existing approaches to provide token-level rewards fail to account for varying degrees of preference inherent to each token. |
| Approach: | They propose a reward model that uses a discriminator to assign token-based continuous rewards to each token considering the context. |
| Outcome: | Extensive experiments show that the proposed reward model improves on open-ended language generation benchmarks. |
Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM (2024.findings-emnlp)
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| Challenge: | Existing models that capture multiple modalities with a single input length are unable to handle this computational burden. |
| Approach: | They propose a query-based cross-modal projector that compresses visual tokens based on input through the cross-attention mechanism. |
| Outcome: | The proposed projector reduces the need for manually designing the 2D scan order of original image features when converting them into an input sequence for Mamba LLMs. |
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation (2023.findings-emnlp)
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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
| Challenge: | Existing knowledge-grounded dialogue generation algorithms require annotated knowledge to generate a response grounded on the retrieved knowledge. |
| Approach: | They propose an efficient algorithm for latent variable modeling that leverages large amount of dialogue data. |
| Outcome: | The proposed algorithm outperforms the supervised learning algorithm on knowledge-grounded dialogue datasets while maintaining efficiency and scalability. |
Selective Token Generation for Few-shot Natural Language Generation (2022.coling-1)
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| Challenge: | Experimental results show that the proposed selective token generation algorithm outperforms the previous additive learning algorithms based on the PLMs. |
| Approach: | They propose an additive learning algorithm that selectively outputs language tokens between a task-general PLM and a specific adapter during training and inference. |
| Outcome: | The proposed algorithm outperforms existing methods on few-shot natural language generation tasks. |
VisEscape: A Benchmark for Evaluating Exploration-driven Decision-making in Virtual Escape Rooms (2025.emnlp-main)
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| Challenge: | Existing studies on embodied agents have addressed the importance of exploration in environments where tasks and solutions are not predefined. |
| Approach: | They propose a virtual escape room that evaluates AI models in a dynamic environment . they propose to integrate memory management and reasoning into the simulation . |
| Outcome: | The proposed model improves in dynamic and exploration-driven environments by integrating memory management and reasoning. |