24 citations · 46 across the 10 of their papers we have counts for
10 papers
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…
Affective AI Safety: The Missing Piece in LLM Safety
Carolin Ifländer, Alba Curry, Flor Miriam Plaza-del-Arco +1
AI safety research has focused predominantly on epistemic and physical harms (e.g., misinformation, bias, system reliability) while the risks that arise from AI systems' engagement…
Responsible Evaluation of AI for Mental Health
Hiba Arnaout, Anmol Goel, H. Andrew Schwartz +13
Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned wi…
No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models
Flor Miriam Plaza-del-Arco, Paul Röttger, Nino Scherrer +3
Large language models (LLMs) are increasingly integrated into our daily lives and personalized. However, LLM personalization might also increase unintended side effects. Recent wor…
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Paul Röttger, Giuseppe Attanasio, Felix Friedrich +19
Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards,…
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
Justin Zhao, Flor Miriam Plaza-del-Arco, Benjamin Genchel +1
As Large Language Models (LLMs) continue to evolve, evaluating them remains a persistent challenge. Many recent evaluations use LLMs as judges to score outputs from other LLMs, oft…
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
Flor Miriam Plaza-del-Arco, Amanda Cercas Curry, Alba Curry +2
Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP appli…
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
Giuseppe Attanasio, Flor Miriam Plaza-del-Arco, Debora Nozza +1
Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often…
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
Flor Miriam Plaza-del-Arco, María-Teresa Martín-Valdivia, Roman Klinger
Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This confl…
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language
Flor Miriam Plaza-del-Arco, Sercan Halat, Sebastian Padó +1
The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task to decide if a text contains HOF. We investigate whether HOF detection c…