Papers by Woojeong Kim
Analyzing Norm Violations in Live-Stream Chat (2023.emnlp-main)
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Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
| Challenge: | Existing methods for detecting toxic language and norm violations are limited to live-streaming platforms . existing methods are less effective when applied to live streaming platforms based on a limited time frame . |
| Approach: | They propose to use contextual information to automatically moderate toxic content on live streaming platforms. |
| Outcome: | The proposed model improves on live-streaming platforms by 35%. |
DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues (2025.findings-acl)
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| Challenge: | Existing function-calling benchmarks focus on single-turn interactions but ignore complexity of real-world scenarios. |
| Approach: | They propose a framework that constructs practical function-calling datasets by synthesizing conversations through a tool graph that maintains dependencies across rounds. |
| Outcome: | The proposed framework synthesizes conversations through a tool graph that maintains dependencies across rounds and a multi-agent system with distinct personas to enhance dialogue naturalness. |
From Chat Logs to Collective Insights: Aggregative Question Answering (2025.emnlp-main)
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| Challenge: | Existing approaches to analyzing large-scale conversation logs treat interactions as independent, missing critical insights. |
| Approach: | They propose a task that requires models to reason explicitly over thousands of user-chatbot interactions to answer aggregational queries. |
| Outcome: | The proposed task requires models to reason over thousands of user-chatbot interactions to answer aggregational queries such as identifying emerging concerns among demographics. |
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data (2021.acl-long)
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| Challenge: | Existing automated forecasting studies rely on structured data to predict future events. |
| Approach: | They propose a question-answering task that limits access to unstructured text data . they use a crowdsourced dataset to form a restricted-domain, multiple-choice, question-announcement task . |
| Outcome: | The proposed model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%. |