R a PSIL : A P reference‐ G uided Interview Agent for R apport‐ A ware S elf‐ D isclosure (2025)

Abstract: Facilitating self-disclosure without causing discomfort remains a difficult task—especially for AI systems. In real-world applications such as career counseling, wellbeing support, and onboarding interviews, eliciting personal information like concerns, goals, and personality traits is essential. However, asking such questions directly often leads to discomfort and disengagement. We address this issue with RaPSIL (Rapport-aware Preference-guided Self-disclosure Interview Learner), a two-stage LLM-based system that fosters natural, engaging conversations to promote self-disclosure. In the first stage, RaPSIL selectively imitates interviewer utterances that have been evaluated by LLMs for both strategic effectiveness and social sensitivity. It leverages LLMs as multi-perspective judges in this selection process. In the second stage, it conducts self-play simulations, using the Reflexion framework to analyze failures and expand a database with both successful and problematic utterances. This dual learning process allows RaPSIL to go beyond simple imitation, improving its ability to handle sensitive topics naturally by learning from both successful and failed utterances. In a comprehensive evaluation with real users, RaPSIL outperformed baselines in enjoyability, warmth, and willingness to re-engage, while also capturing self-descriptions more accurately. Notably, its impression scores remained stable even during prolonged interactions, demonstrating its ability to balance rapport building with effective information elicitation. These results show that RaPSIL enables socially aware AI interviewers capable of eliciting sensitive personal information while maintaining user trust and comfort—an essential capability for real-world dialogue systems.

## introduction simulating humans accurately is an important issue in the nlp and ai fields, especially for applications that aim to replicate human attitudes and behavior @xcite . these applications often require deep personal information, such as individual values and life philosophies, which play a key role in improving personalization and supporting self-understanding @xcite . however, collecting such information can be challenging. traditional questionnaires impose a cognitive burden on those with limited self-analytical skills @xcite , and often yield superficial responses due to social desirability bias @xcite . while dialogue-based methods such as interviews can elicit more authentic self-disclosures @xcite , they require skilled interviewers and significant time investment, which limits their scalability. this has created a need for scalable, socially aware systems that can promote selfdisclosure in natural dialogue. while dialogue systems can address these scalability challenges, their eyective implementation presents unique obstacles. human interviewers naturally employ strategies of beginning with superficial topics and gradually transitioning to deeper subjects after establishing rapport @xcite . when systems lack this capability, users ex-perience discomfort, resulting in decreased engagement and compromised information accuracy @xcite ). an ideal dialogue system must maintain positive user impressions while successfully eliciting deep selfrecognition information, particularly in initial interactions. current dialogue systems use various approaches to elicit information: some implement strategic patterns like interspersing casual conversation between questions or gradually progressing from superficial to deeper topics @xcite , while others employ imitation learning from human dialogue logs @xcite . however, these methods often struggle to adapt to general information-gathering scenarios. while recent advances in llms have enabled prompt-based dialogue system design @xcite , creating systems that can flexibly adapt strategies to maintain user engagement while eliciting deeper personal information remains an area requiring further exploration @xcite . to address this challenge, we propose rapsil, a preference-guided interview agent that balances positive user impressions with effective information elicitation through two approaches: (1) selective imitation learning from human-to-human interview dialogue datasets, using an llm to identify and learn from only highly-rated utterances @xcite , and (2) simulation-enhanced learning that generates diverse llm-to-llm dialogues to acquire adaptable conversation strategies despite limited real data @xcite . in addition, failed utterances in the simulations are analyzed using reflexion @xcite , and improved versions are stored in a database of few-shot examples. these examples are retrieved in similar contexts to guide the model, enabling it to learn not only from good responses but also from failures in challenging scenarios. figure 1 illustrates an example of the dialogue strategy adopted by rapsil to promote self-disclosure. we evaluated our approach through experiments with 30 participants, tasking the system to elicit information across five personal categories (interests, dislikes, work, personality traits, and life goals). among these, the "dislikes," "personality traits," and "life goals" categories are often sensitive in nature and typically require some level of rapport before users feel comfortable disclosing them. simply asking direct questions about these topics may lead to reduced engagement, even if information is successfully obtained. this makes the task particularly challenging, as systems must manage both strategic questioning and user comfort. the study compared four models: a baseline prompt-based model, a simple imitation learning model, a dpooptimized model @xcite , and rap-sil. evaluation included (1) subjective engagement assessment, (2) objective information extraction accuracy using self-description reproducibility, and (3) turn-by-turn strategic quality assessment. the results strongly supported our hypotheses. rapsil successfully elicited personal information while maintaining engagement and establishing rapport. it showed statistically significant improvements in subjective metrics (particularly reengagement willingness and warmth) and achieved the highest recall and f1 scores in ragchecker @xcite evaluations. unlike other methods that declined in quality over time, our approach maintained high dialogue quality throughout extended interactions, demonstrating its potential for realworld long-term user engagement applications. the contributions of this study are as follows: ## related work this section explores interview dialogue systems and learning methods for socially adaptive conversational agents. ## proposed method we propose rapsil, an llm-based dialogue system for continuous interviews, using a two-stage learning approach (fig. 2 ). ## evaluations and results in this section, we evaluate the effectiveness of rapsil across three dimensions: user impressions, information extraction accuracy, and dialogue quality over time. ## discussion to further analyze the types of information elicited by each system, we categorized the claims extracted by ragchecker using gpt-4o-mini into five semantic categories. the aggregated results are shown in representative examples of extracted claims for each category are shown in appendix e a potential concern with such off-topic exchanges is the risk of reduced strategic quality. however, figure 4c shows that rapsil maintained high strategy scores throughout the dialogue, comparable to or better than other methods. this suggests that rapsil engages in off-topic conversation not arbitrarily, but as a deliberate strategy to enhance engagement while preserving information elicitation goals. in short, rapsil demonstrates the ability to integrate small talk in a context-aware and goal-sensitive manner, contributing to both user engagement and effective information gathering. ## conclusion this study demonstrated a method for building dialogue systems that can naturally elicit deep personal information-such as concerns, personality, and life goals-without reducing user engagement. by combining imitation learning with simulationenhanced refinement, our approach enables the model to acquire conversational techniques like casual chatting and empathetic phrasing, which contribute to both user comfort and strategic information gathering. experimental results confirmed that our system successfully extracted accurate, self-consistent information aligned with users' self-descriptions while maintaining high engagement scores. these findings highlight the potential of our approach for improving the personalization accuracy of dialogue systems by fostering user self-disclosure through enjoyable and natural conversations. in particular, it shows strong potential as a foundational technology for practical systems such as interview agents and personalized assistants that require sustained user engagement to build a deeper understanding of individuals.