Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two.Yet, real-world conversations naturally involve fluid transitions between these modes.To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transitionaware dialogue modeling that incorporates structurally diverse and integrated mode flows.TACT supports both user-and agent-driven mode switches, enabling robust modeling of complex conversational dynamics.To evaluate an agent's ability to initiate and recover from mode transitions, we propose two new metrics-Switch and Recovery.Models trained on TACT outperform baselines in both intent detection and mode transition handling.Moreover, applying Direct Preference Optimization (DPO) to TACT-trained models yields additional gains, achieving 75.74% joint mode-intent accuracy and a 70.1% win rate against GPT-4O in human evaluation.These results demonstrate that pairing structurally diverse data with DPO enhances response quality and transition control, paving the way for more proactive and transition-aware conversational agents.

To understand users' risk perceptions about sharing their PHR on SNS, we first conducted a qualitative user study by interviewing 16 participants. Next, we conducted a large-scale online user study with 497 participants in the U.S. to validate our qualitative results from the first study. Our study results show that a majority of users do not show strong motivation of sharing PHR on SNS due to several concerns such as misuse/abuse and security issues of shared PHR. In particular, participants are highly concerned about sharing diseases and diagnostic test results than other types of PHRs (e.g., details of hospital visits and medical interviews). However, we found about 55.13% of the participants have the experiences of sharing their health-related information on SNS. Also, we learned that users' sharing behavior for disease data can be significantly influenced by the severity/type of the disease as well as preferred recipients. Based on our findings, we propose a recommendation method to automatically determine whether users' posts can be shared with everyone on SNS by analyzing the keywords frequently occurred in health-related posts. Our implementation using Random Forest achieved an F-measure of 87.4%, indicating that PHR can be restrictively shared with high accuracy, when sharing health-related posts on SNS.