activity
20242026
most citedMindEval: Benchmarking Language Models on Multi-turn Mental Health Support

3 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Risk Governance for Generative AI Mental Health Support: A Multi-Turn Safety Architecture

Anabela C. Areias, Catarina Botelho, António Farinhas +7

Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk. Existing safety approaches primari…

cs.AI2026

MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support

António Farinhas, Nuno M. Guerreiro, José Pombal +6

Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose s…

cs.CL2025★ 3 cited

MindEval: Benchmarking Language Models on Multi-turn Mental Health Support

José Pombal, Maya D'Eon, Nuno M. Guerreiro +3

Demand for mental health support through AI chatbots is surging, though current systems present several limitations, like sycophancy or overvalidation, and reinforcement of maladap…

cs.CV2025

How Well Do Vision-Language Models Understand Movies?

Emmanouil Zaranis, António Farinhas, Saul Santos +28

Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos--but can they understand the narrative structure of movies as humans…

cs.CL2025

Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral

António Farinhas, Nuno M. Guerreiro, Sweta Agrawal +2

Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some i…

cs.CV2025

-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation

Saul Santos, António Farinhas, Daniel C. McNamee +1

Current video-language models struggle with long-video understanding due to limited context lengths and reliance on sparse frame subsampling, often leading to information loss. Thi…

cs.CL2024

Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation

Sweta Agrawal, José G. C. de Souza, Ricardo Rei +5

Alignment with human preferences is an important step in developing accurate and safe large language models. This is no exception in machine translation (MT), where better handling…