Papers by Cho Min
PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling (2026.findings-acl)
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| Challenge: | Existing MI datasets do not explicitly model structured progression of MI phases, which is essential for effective and goal-oriented counseling. |
| Approach: | They propose a phase-structured MI dataset with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. |
| Outcome: | The proposed model achieves 12.3% better coverage of MI phases, 37.6% in guiding, and 61.1% in choosing. |
A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited due to the need for increased computational resources. |
| Approach: | They propose a method to learn PEFT parameters from data by projecting high dimensional parameters onto low dimensional parameter manifolds or identifying PEFT parametrically. |
| Outcome: | The proposed method can be used to identify PEFT parameters on GLUE tasks. |
Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework (2026.findings-acl)
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| Challenge: | Multi-agent systems utilizing large language models assign authoritative roles to improve performance, yet the impact of authority bias on agent interactions remains underexplored. |
| Approach: | They propose to classify authoritative roles into legitimate, referent, and expert types and analyze their influence across 12-turn conversations using French and Raven’s power-based theory. |
| Outcome: | The proposed model enables agents to perform better in multi-agent evaluations. |