Papers by Taeyoon Kim
What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue (2023.acl-industry)
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| Challenge: | a personalized dialogue system can generate user-customized responses based on long-term memory about the user's persona. |
| Approach: | They propose a method for building a personalized open-domain dialogue system . they combine weighted dataset blending and negative persona information augmentation methods . |
| Outcome: | The proposed method balances dialogue fluency and tendency to ground while introducing a response-type label to improve controllability and explainability of the grounded responses. |
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)
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Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo
| Challenge: | a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers . |
| Approach: | They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context. |
| Outcome: | The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. |
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. |
Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)
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Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak, Hyungjoo Chae, Taeyoon Kwon, Yohan Jo, Seung-won Hwang, Dongha Lee, Jinyoung Yeo
| Challenge: | Existing studies focus on getting rid of outdated memories to improve retrieval quality, but we argue that such memories provide rich, important contextual cues for response generation (RG). |
| Approach: | They propose a framework for LLM-based lifelong dialogue agents that discards memory removal and manages large-scale memories by linking them based on their temporal and cause-effect relation. |
| Outcome: | The proposed framework augments RG with memory timelines based on evolution or causality of relevant past events. |
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models (2024.emnlp-main)
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Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Ong, Beong-woo Kwak, Moohyeon Kim, Sunghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo
| Challenge: | Prior work has used LLMs to generate programming language and applied external compilers for such tasks. |
| Approach: | They propose a framework that expresses task-level logic with pseudocode and tailors it to each instance and simulates execution of it. |
| Outcome: | The proposed framework outperforms baselines in diverse reasoning tasks. |
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization (2025.acl-long)
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| Challenge: | Existing benchmarks for reward models show a weak correlation with performance of optimized policies . existing benchmarks do not accurately assess the true capabilities of reward models . |
| Approach: | They explore how reward overoptimization captures how well a reward model aligns with human preferences and the dynamics of the learning signal it provides to the policy. |
| Outcome: | The proposed benchmarks show that reward overoptimization is a weak factor . the high correlation with degree of overoptimalization leads to lower correlation with downstream performance . |