collaborators

6 papers

cs.HC2026

Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support

Natalia Amat-Lefort, Mert Yazan, Amanda Cercas Curry +1

Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric…

cs.HC2026

Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI

Mert Yazan, Suzan Verberne, Frederik Bungaran Ishak Situmeang

Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, referred to as contextualization…

cs.HC2026

The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search

Mert Yazan, Frederik Bungaran Ishak Situmeang, Suzan Verberne

Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance when users blindly trust AI and a…

cs.CL2026

From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support

Natalia Amat-Lefort, Mert Yazan, Amanda Cercas Curry +1

Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional support. Yet what drives ado…

cs.HC2025

Personality over Precision: Exploring the Influence of Human-Likeness on ChatGPT Use for Search

Mert Yazan, Frederik Bungaran Ishak Situmeang, Suzan Verberne

Conversational search interfaces, like ChatGPT, offer an interactive, personalized, and engaging user experience compared to traditional search. On the downside, they are prone to…

cs.IR2025

Improving RAG for Personalization with Author Features and Contrastive Examples

Mert Yazan, Suzan Verberne, Frederik Situmeang

Personalization with retrieval-augmented generation (RAG) often fails to capture fine-grained features of authors, making it hard to identify their unique traits. To enrich the RAG…