Papers by Hyunji Park
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams (2026.acl-long)
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Jiyeon Kim, Hyunji Lee, Dylan Zhou, Sue Hyun Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Sungmin Cha, Minjoon Seo
| Challenge: | Existing models and agentic memory systems fail to adapt robustly to OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments. |
| Approach: | They propose a benchmark to evaluate models' ability to adapt to changing knowledge over streaming . they use two datasets to analyze how facts evolve over time . |
| Outcome: | The proposed benchmark evaluates models in an online adaptation setting over streaming, continually updating knowledge. |
PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling (2026.findings-eacl)
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| Challenge: | Existing approaches to mental health dialogue are reactive and lack systematic user state modeling for proactive therapeutic exploration. |
| Approach: | They propose a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework augmented with cognitive error detection. |
| Outcome: | The proposed system outperforms baseline and ablation modes in automatic evaluation and expert evaluation by a certified counselor. |
Efficient Classification of Long Documents Using Transformers (2022.acl-short)
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| Challenge: | Several methods have been proposed for classifying long textual documents using Transformers, but there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. |
| Approach: | They propose to use a dataset to evaluate the relative efficacy of various models for long document classification using Transformers. |
| Outcome: | The proposed models outperform simple baseline models and yield inconsistent performance across datasets. |