Papers by Seungbeen Lee
Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games (2025.acl-long)
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| Challenge: | Text-based interactive environments have long presented formidable challenges for AI. |
| Approach: | They propose a method for projecting human personality traits onto agents to guide their behavior and integrate them into their policy-learning pipelines. |
| Outcome: | The proposed method induces personality in a text-based game agent by integrating personality profiles directly into the agent's policy-learning pipeline. |
Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you! (2024.emnlp-main)
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| Challenge: | Existing models lack this active understanding capacity, limiting their applicability in real-world scenarios. |
| Approach: | They propose a benchmark to assess the impact of multimodal inputs on lexical ambiguities. |
| Outcome: | The proposed benchmark assesses the impact of multimodal inputs on lexical ambiguities. |
Representation Bending for Large Language Model Safety (2025.acl-long)
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Ashkan Yousefpour, Taeheon Kim, Ryan Sungmo Kwon, Seungbeen Lee, Wonje Jeung, Seungju Han, Alvin Wan, Harrison Ngan, Youngjae Yu, Jonghyun Choi
| Challenge: | Existing safety-enhancing techniques, such as fine-tuning with human feedback or adversarial training, are still vulnerable as they address specific threats and fail to generalize across unseen attacks. |
| Approach: | They propose a new approach that disrupts representations underlying harmful behaviors in Large Language Models by using loss-based fine-tuning. |
| Outcome: | The proposed approach outperforms existing methods such as Circuit Breaker, RMU, and NPO with 95% reduction in attack success rates across diverse jailbreak benchmarks. |
Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory (2024.findings-emnlp)
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Suyeon Lee, Sunghwan Kim, Minju Kim, Dongjin Kang, Dongil Yang, Harim Kim, Minseok Kang, Dayi Jung, Min Kim, Seungbeen Lee, Kyong-Mee Chung, Youngjae Yu, Dongha Lee, Jinyoung Yeo
| Challenge: | Existing models that use large language models are not available due to ethical concerns, and data privacy concerns are a concern. |
| Approach: | They propose a multi-turn dialogue dataset that emulates real-life counseling interactions using the goal-oriented approach of Cognitive Behavioral Therapy (CBT). |
| Outcome: | The proposed model outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent. |
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor (2026.acl-long)
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Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka, Seungbeen Lee, Jaeyoung Lee, Hyeju Jang, Alice Oh, Youngjae Yu
| Challenge: | Large Language Models (LLMs) absorb social and cultural biases embedded in vast web-scale corpora and are increasingly deployed in high-stakes domains such as hiring, education, and law. |
| Approach: | They propose a framework to investigate counterfactual unfairness through humor by observing how the model’s responses change when we swap who speaks and who is addressed while holding other factors constant. |
| Outcome: | The proposed framework covers humor generation refusal, speaker intention inference, and relational/societal impact prediction tasks. |
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)
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Seungbeen Lee, Seungwon Lim, Seungju Han, Giyeong Oh, Hyungjoo Chae, Jiwan Chung, Minju Kim, Beong-woo Kwak, Yeonsoo Lee, Dongha Lee, Jinyoung Yeo, Youngjae Yu
| Challenge: | Recent advances in Large Language Models (LLMs) have led to their adaptation as conversational agents. |
| Approach: | They propose a new benchmark that uses 8K multi-choice questions to assess the personality of Large Language Models. |
| Outcome: | The proposed personality test outperforms existing personality tests for LLMs in reliability and validity. |