collaborators

18 papers

cs.CL2026

Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers

Vera Neplenbroek, Gabriele Sarti, Arianna Bisazza +2

When large language models (LLMs) are used in high-stakes scenarios, such as legal, medical and financial advice, even a single conversation history is enough to drive differences…

cs.CL2026

Post-Training Language Models for Crosslingual Consistency

Tianyu Liu, Jirui Qi, Mrinmaya Sachan +3

Language models often respond inconsistently to translation-equivalent prompts across languages, undermining the reliability of multilingual systems. To quantify this, we give an i…

cs.CL2026

Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation

Joris Baan, Wilker Aziz, Barbara Plank +1

Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling K responses yields a belief state - res…

cs.CV2026

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

Anna Bavaresco, Ina Klarić, Raquel Fernández +1

Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivi…

cs.CV2026

Where is the multimodal goal post? On the Ability of Foundation Models to Recognize Contextually Important Moments

Aditya K Surikuchi, Raquel Fernández, Sandro Pezzelle

Foundation models are used for many real-world applications involving language generation from temporally-ordered multimodal events. In this work, we study the ability of models to…

cs.CL2026

Vision-Language Models Align with Human Neural Representations in Concept Processing

Anna Bavaresco, Marianne de Heer Kloots, Sandro Pezzelle +1

Recent studies suggest that transformer-based vision-language models (VLMs) capture the multimodality of concept processing in the human brain. However, a systematic evaluation exp…