EMNLP 2026Main
ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?
Tracking changes in a character's values, motivations, and relationships improves role-play fidelity.
I'm an integrated M.S.–Ph.D. student in Data Science at Seoul National University, advised by Yohan Jo in the HOLI Lab. I study the behavioral evaluation and alignment of language agents. My research examines whether assessments of values and behavioral tendencies predict responses in realistic interactions, how faithfully language models represent human traits and evolving personas, and how agents respond to challenging users and unreliable tools. Through benchmark design and behavioral interventions, I aim to develop agents whose behavior more closely aligns with human needs and values.
opusdeisong [at] snu [dot] ac.kr
ArcANE and Human Psychometric Questionnaires Mischaracterize LLM Behavior accepted to EMNLP 2026 (Main).
Awarded the Presidential Science Scholarship for Graduate Students (2026–2027) by the Ministry of Science and ICT.
PA-Tool accepted to ACL 2026 (Main).
Psychometric Item Validation Using Virtual Respondents accepted to TACL.
Non-Collaborative User Simulators for Tool Agents accepted to ICLR 2026.
Quantifying Data Contamination in Psychometric Evaluations of LLMs accepted to EACL 2026 Findings.
Value Portrait accepted to ACL 2025 (Main).
EMNLP 2026Main
Tracking changes in a character's values, motivations, and relationships improves role-play fidelity.
EMNLP 2026Main
Questionnaire-based value and personality profiles diverge from behavior on realistic user queries.
ACL 2026Main
Adapting tool schemas to models' pretrained naming conventions improves tool-use accuracy without retraining.
EACL 2026Findings
Widely used psychometric inventories show substantial contamination, and models can adjust responses to match requested scores.