Papers by Deuksin Kwon
Can Vision Language Models Understand Mimed Actions? (2025.findings-acl)
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Hyundong Justin Cho, Spencer Lin, Tejas Srinivasan, Michael Saxon, Deuksin Kwon, Natali T. Chavez, Jonathan May
| Challenge: | Nonverbal communication (NVC) is an integral part of human language, but it has been overlooked in natural language processing research. |
| Approach: | They propose a multimodal multimodal recognition task that uses a corpus of mimed gestures to evaluate their understanding of NVC. |
| Outcome: | The proposed task is based on 86 unique gestures with perturbations applied to avatar, background, and viewpoint for evaluating recognition robustness. |
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly being utilized as AI negotiation agents . however, prior research on LLMs lacks a systematic evaluation of their diverse capabilities in negotiation. |
| Approach: | They propose to analyze the multifaceted capabilities of Large Language Models (LLMs) across diverse dialogue scenarios throughout the stages of a typical negotiation interaction. |
| Outcome: | The proposed model outperforms GPT-4 in many negotiation tasks while identifying specific challenges, such as making subjective assessments and generating contextually appropriate, strategically advantageous responses. |
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. |
ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization (2025.emnlp-main)
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| Challenge: | Existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. |
| Approach: | They propose a framework for turn-level offer optimization based on two core principles: opponent modeling and Tit-for-Tat reciprocity. |
| Outcome: | The proposed framework outperforms baselines across diverse partner agents and validates through human evaluation. |
Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. |
| Approach: | They examine alignment of personality-prompted Large Language Models in conflict dialogues that incorporate negotiation by simulating a five-factor personality profile. |
| Outcome: | The proposed model achieves the closest alignment with humans in linguistic style and emotional dynamics while Claude-3.7-Sonnet best reflects strategic behavior. |