Day: September 11, 2026

Arabella Sinclair, Assistant Professor at UCL (London) and at the University of Aberdeen will present her work who will present her work on large language models (LLMs), language processing, and cognition.

Abstract
Both humans and Large Language Models (LMs) generate predictions about upcoming words and structures based on recent context. In dialogue, speakers continuously choose how to realise their communicative intentions, with these choices shaped by multiple, sometimes competing pressures, including production costs borne by the speaker and comprehension costs incurred by the listener. Production costs reflect the effort involved in planning and generating an utterance, while comprehension costs reflect how easily an utterance can be processed and interpreted. These costs are influenced by a range of factors, including priming, utterance length, informational content, and contextual predictability.
In this talk, I explore how LMs can serve both as tools for studying human interaction and as components of broader cognitive models of language processing. First, I present evidence for parallels between humans and LMs in primed comprehension facilitation, showing how prior exposure to linguistic structures influences subsequent processing. Second, I discuss ongoing work investigating analogous effects in a controlled production setting. Finally, I introduce a new procedure for constructing contextual alternative sets that enables probabilistic pragmatic models of language production to be instantiated and evaluated at scale in open-ended communicative settings. This framework also provides a principled basis for comparing competing notions of communicative cost.
Taken together, these findings shed light on the mechanisms underlying LMs’ in-context learning behaviour while also assessing their potential as models of human linguistic processing and communicative decision-making.

When: 11/09/2026, 10:30
Where: Sala Conferenze, 3rd Floor

Bernardo Magnini, Senior Researcher at Fondazione Bruno Kessler (FBK) in Trento, will present a series of results from the eCREAM (enabling Clinical Research in Emergency and Acute care Medicine) Horizon project.

Abstract
This seminar provides an overview of the use of Large Language Models (LLMs) within the eCREAM (enabling Clinical Research in Emergency and Acute care Medicine) Horizon project. I will first introduce a unique corpus of clinical notes collected from the emergency departments of several Italian hospitals and show how these data have been used to train small LLMs for a range of clinical tasks. I will then focus on Case Report Form (CRF) filling, a key task addressed in eCREAM, where manually annotated clinical notes are leveraged to extract clinically relevant information. I will present results showing that substantial performance improvements can be achieved by fine-tuning models with additional training data automatically extracted from unannotated clinical notes. Finally, I will discuss ongoing work on the automatic interpretation of patient–doctor interactions in emergency departments, highlighting current challenges and outlining future research directions.

Where: Sala Conferenze, 3rd Floor
When: 11/09/2026, 9:30

Seyed Mahed Mousavi from University of Trento will give a seminar to our Department.

Abstract
Conversational AI systems can now produce fluent, human-like text and are increasingly used in everyday and sensitive settings. This talk argues that fluency should not be mistaken for contextual understanding, and that current AI systems are not trained to track who they are talking to, what has happened before, or what is socially appropriate in a given situation. 

The talk presents two lines of work addressing this problem. The first focuses on deploying ConvAI models for longitudinal dialogues, i.e. a sparse sequence of personal dialogue sessions. It presents methods for collecting multi-session conversations, building and updating a model of the individual user over time, and evaluating AI models in a standardized way. These efforts resulted in the first registered Randomized Controlled Trial (RCT) with a ConvAI system in the mental health domain. The second line of work asks why AI systems struggle to understand context in the first place. It shows that many benchmarks used to claim reasoning abilities in AI are themselves flawed, and traces the problem to how these systems are trained, showing evidence that the current training paradigm is not sustainable, and that loss optimization is not a reliable signal of learning.

When: Tuesday, 22nd of September, 14:00
Where: Sala Conferenze, 3rd floor