Towards empathy tuning of LLM-based conversational agent
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings 2026 IEEE 34th International Requirements Engineering Conference Workshops. REW 2026
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
469-476
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
Montreal
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Empathy is increasingly incorporated into large language model (LLM)–based conversational systems, particularly in healthcare settings. However, a persistent gap remains between the empathy expressed by these systems and the empathy expected and perceived by end users. This misalignment limits the effectiveness, trust, and acceptance of empathic AI, especially in emotionally sensitive domains such as cancer support. To address this challenge, this paper proposes an initial empathy-tuning for developers to systematically align system-delivered empathy with user expectations. Central to this approach is the assumption that empathy is not one-size-fits-all, as users require different forms and intensities of empathy depending on context and timing. We explore empathy tuning through a structured, developer-guided pipeline and demonstrate it via a prototypical implementation using the EPITOME framework. The approach is evaluated with prompt-based experiments and initial user studies in cancer-related scenarios. Our findings provide preliminary evidence that empathy in LLM-based systems can be tuned and that calibrated empathy improves alignment between system-generated and user-perceived empathy, while also highlighting the need for dynamic, runtime adaptation of empathic behaviour depending on conversation content.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
2026 IEEE 34th International Requirements Engineering Conference Workshops (REW)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
17.08.2026
Enddatum der Konferenz
21.08.2026
Datum der letzten Prüfung
ISBN
979-8-3195-4535-0
979-8-3195-4536-7
979-8-3195-4536-7
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Zitation
Yeganeh, M., Fricker, S., Leber, T., & Barber, D. (2026). Towards empathy tuning of LLM-based conversational agent. Proceedings 2026 IEEE 34th International Requirements Engineering Conference Workshops. REW 2026, 469–476. https://doi.org/10.1109/rew72749.2026.00082