Papers by Hae Won Park
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
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| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication (2025.emnlp-main)
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| Challenge: | a recent study examines the role of personalization in enabling LLMs to serve as effective mediators in human communication for authentic connection. |
| Approach: | They leverage nonviolent communication theory to evaluate LLMs in detecting conversational breakdowns . they annotate a subset of dialogues and obtain fine-grained labels of communication breakdown types . |
| Outcome: | The proposed dataset analyzes human interactions and relationships in a human context. |
BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)
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Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
| Challenge: | Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior . |
| Approach: | They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection. |
| Outcome: | The proposed training strategy boosts performance on both benchmarks. |
Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition (2025.findings-emnlp)
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| Challenge: | a large language model is used to decompose global feedback into a lightweight reward model. |
| Approach: | They propose a large language model based reward decomposition framework for dialogue agents . they use a frozen large language modeling framework to decompose global feedback . |
| Outcome: | The proposed framework infers fine-grained local rewards from a single session-level feedback signal. |
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs (2026.findings-acl)
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Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi
| Challenge: | Reinforcement learning (RL) is a paradigm for post-training large language models, but it suffers from exploration collapse . a new study finds that RL fails to reward correct solutions that exhibit rare high-level strategies . |
| Approach: | They propose a method that rewards correct solutions that exhibit rare high-level strategies by clustering rollouts according to their high- level solution strategies. |
| Outcome: | The proposed approach improves pass@k across large sampling budgets and increases area under the pass@K curve (AUC@K) without sacrificing pass@1. |