@inproceedings{hama-etal-2025-rapsil,
title = "{R}a{PSIL}: A {P}reference{-}{G}uided Interview Agent for {R}apport{-}{A}ware {S}elf{-}{D}isclosure",
author = "Hama, Kenta and
Otsuka, Atsushi and
Mizukami, Masahiro and
Sugiyama, Hiroaki and
Naka, Makoto",
editor = "B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
Asher, Nicholas and
Kim, Seokhwan and
Merlin, Teva",
booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2025",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigdial-1.29/",
pages = "355--369",
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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hama-etal-2025-rapsil">
<titleInfo>
<title>RaPSIL: A Preference-Guided Interview Agent for Rapport-Aware Self-Disclosure</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kenta</namePart>
<namePart type="family">Hama</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Atsushi</namePart>
<namePart type="family">Otsuka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Masahiro</namePart>
<namePart type="family">Mizukami</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroaki</namePart>
<namePart type="family">Sugiyama</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Makoto</namePart>
<namePart type="family">Naka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
</titleInfo>
<name type="personal">
<namePart type="given">Frédéric</namePart>
<namePart type="family">Béchet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fabrice</namePart>
<namePart type="family">Lefèvre</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nicholas</namePart>
<namePart type="family">Asher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Seokhwan</namePart>
<namePart type="family">Kim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Teva</namePart>
<namePart type="family">Merlin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Avignon, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">hama-etal-2025-rapsil</identifier>
<location>
<url>https://aclanthology.org/2025.sigdial-1.29/</url>
</location>
<part>
<date>2025-08</date>
<extent unit="page">
<start>355</start>
<end>369</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RaPSIL: A Preference-Guided Interview Agent for Rapport-Aware Self-Disclosure
%A Hama, Kenta
%A Otsuka, Atsushi
%A Mizukami, Masahiro
%A Sugiyama, Hiroaki
%A Naka, Makoto
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F hama-etal-2025-rapsil
%X 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.
%U https://aclanthology.org/2025.sigdial-1.29/
%P 355-369
Markdown (Informal)
[RaPSIL: A Preference‐Guided Interview Agent for Rapport‐Aware Self‐Disclosure](https://aclanthology.org/2025.sigdial-1.29/) (Hama et al., SIGDIAL 2025)
ACL