• Training machines to communicate, interact and benefit humanity.

News

AI in Medicine for Med Students

Today was a big feel-good day for me and my colleagues Giovanni Iacca and Eleonora Aiello from the Department of Computer Science and Information Engineering at the University of Trento.

Our students' presentations delved into specific aspects of how medical professionals, from ophthalmology, neurosurgery, cardiology, radiology, endovascular surgery, and health physics, could benefit from AI systems. 

They showcased how AI may help reduce the burnout of radiologists, support neurosurgeons in improving the precision in delivering brain stimulation for Parkinson's patients, detect postoperative delirium, improve the tracking of white matter pathways, predict toxicity in radiotherapy, and many more high-impact issues in medicine and health.

Go students, enjoy the rest of the ride!

 

 

Talk: "Conversational AI to Benefit Individuals" by Giuseppe Riccardi May 29th at 4pm (italian time) at Manchester Open NLPclub.

Research in human-machine dialogue (aka ConvAI) has been driven by the quest for open-domain, knowledgeable and multimodal agents. In contrast, the complex problem of designing , training and evaluating a conversational system and its components is currently reduced to a) prompting LLMs, b) coarse evaluation of machine responses and c) poor management of the affective signals. In this talk, we will review the current state-of-the-art in human-machine dialogue research and its limitations. We will present the most challenging frontiers of conversational AI when the objective is to create personal conversational systems that benefit individuals. In this context we will report experiments and RCT trials of so-called personal healthcare agents supporting individuals and healthcare professionals.

2025 Funded PhD and RA Openings

At the Signals and Interactive Systems Lab (University of Trento, Italy) we are looking for highly motivated and talented graduate students to join our research team and work on Conversational Artificial Intelligence.

Conversational Artificial Intelligence includes the following research areas:

- Natural Language Processing
- Conversational Modeling and Systems
- Machine Learning
- Affective Computing

All news

Our Vision


We do research in the analysis and interpretation of signals generated and interpreted in human dialogue and interaction.

We design and train machine learning models to interpret these signals and support the inference process of human-machine dialogue and interaction systems. The design of the human-dialogue system is motivated by the goal of benefiting humans and evaluation methodologies and processes are a high priority in our lab. Our research and achievements come from collaborating with fantastic international researchers, professionals, non-profit organizations, and companies of all sizes.






Our Team

Open Thesis and Internships

Theory of Mind for LLMs ?

Theory of mind (ToM) is very much needed in humans to engage in social activity. Theory of mind is the ability to perceive and track the mental state of others. An example of ToM is the ability of humans to be empathic, that is the ability to reach the so-called self-other overlap state ( aka “put yourself in the other shoes” but it is more complex than that ). During the thesis/internship, you will assess the critical aspects of ToM in machines and neural models.

Generative AI vs. Information Retrieval

Join our cutting-edge research to fine-tune and evaluate state-of-the-art large language models (LLMs) for personalized response generation, and compare their performance against traditional response selection models. Gain hands-on experience, and contribute to groundbreaking advancements in Multimodal Human-Machine Dialogue and Interaction. Ideal for students and interns passionate about AI and machine learning!

Are LLMs Knowledgeable?

If LLMs answer factual questions, does it mean they are knowledgeable? Join our research on evaluating the uncertainty and accuracy of state-of-the-art LLMs in retrieving factual knowledge from their parameters. By analyzing their learned information during training, the study aims to enhance the understanding and reliability of LLMs in real-world applications. Ideal for students passionate about uncertainty and model evaluation.

Publications

Filter:
All
Journals
Conferences
Books

  • Mousavi. M. S., Alghisi S. and Riccardi G., “ DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs  ” Proc. Empirical Methods in Natural Language Processing (EMNLP), Miami, 2024.

  • Roccabruna G., Rizzoli M. and Riccardi G., “ Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?  ” Proc. Empirical Methods in Natural Language Processing (EMNLP), Miami, 2024.

  • Alghisi S., Rizzoli M., Roccabruna G., Mousavi. M. S.and Riccardi G., “ Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue  ” Proc. International Conference on Natural Langauge Generation, Tokyo, 2024.

  • Mousavi. M. S., Roccabruna G., Alghisi S., Rizzoli M., Ravanelli M. and Riccardi G., " Are LLMs Robust for Spoken Dialogues? " Proc. International Workshop on Spoken Dialogue Systems Technology, TALK Sapporo, 2024.
  • Mousavi. M. S., Tanaka, S., Roccabruna G., Yoshino K., Nakamura S. and Riccardi G., " What’s New? Identifying the Unfolding of New Events in a Narrative " ACL, Proc. 5th Workshop of Narrative Understanding, 2023.
  • Mousavi. M. S., Caldarella S. and Riccardi G., " Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps? " ACL, Proc. 5th Workshop on NLP for Conversational AI, 2023.
  • Roccabruna G., Mousavi. M. S. and Riccardi G., " Understanding Emotion Valence is a Joint Deep Learning Task" ACL, Proc. 13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis 2023.
  • Yin M., Roccabruna G., Azad A. and Riccardi G., " Let's Give a Voice to Conversational Agents in Virtual Reality " Proc. INTERSPEECH, Demo Paper , VIDEO Dublin, 2023.
  • Mayor-Torres J., Medina-DeVillers S., Clarkson T., Lerner M.D., Riccardi G. , "Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: a case study in autism ", Artificial Intelligence in Medicine, Volume 143, 2023 .
  • Mousavi M., Roccabruna, Lorandi M., Caldarella S. and Riccardi G., " Evaluation of Response Generation Models: Shouldn’t It Be Shareable and Replicable? " EMNLP Workshop Generation, Evaluation & Metrics (GEM), 2022.
  • Bayerl S., Roccabruna G., Chowdhury A. S., Ciulli T., Danieli M., Riedhammer K. and Riccardi G., " What can Speech and Language Tell us About the Working Alliance in Psychotherapy " Proc. INTERSPEECH, 2022.
  • Mousavi M., Roccabruna G., Tammewar A. Azzolin S. and Riccardi G., " Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives, " ACL, 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 2022.
  • Roccabruna G., Azzolin S. and Riccardi G., " Multi-source Multi-domain Sentiment Analysis with BERT-based Models, " Language Resources and Evaluation Conference, 2022.
  • Tammewar A., Bayerl P. S., Braun F., Riedhammer K. and Riccardi G., " Annotation of Valence for Spoken Personal Narratives, " Language Resources and Evaluation Conference, 2022.
  • Mousavi M., Negro R. and Riccardi G., " An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain, " Eighth Italian Conference on Computational Linguistics, 2022.
  • Danieli M., Ciulli T., Mousavi M. Silvestri G., Barbato S. Di Natale L. and Riccardi G., "Assessing the Impact of Conversational AI in the Treatment of Stress and Anxiety in Ageing Persons: A Randomized Controlled Trial Study ", Journal of Medical Internet Research (JMIR) Mental Health, Vol 9, No 9, 2022 .
  • Mayor-Torres J., Clarkson T., Hauschild K., Luhmann C. C., Lerner D. M. and Riccardi G., "Facial emotions are accurately encoded in the brains of those with autism: A deep learning approach", Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Volume 7, Issue 7, Pages 688-695 2022 .
  • Bayerl P. S., Tammewar A., Riedhammer K. and Riccardi G., " Detecting Emotion Carriers By Combining Acoustic and Lexical Representations, " IEEE Automatic Speech Recognition and Understanding Conference, 2021.
  • Torres M. J., Ravanelli M., Medina-Devilliers S., Lerner D. M. and Riccardi G., " Interpretable SincNet-based Deep Learning for Emotion Recognition in Individuals with Autism, " IEEE Conf. Engineering in Medicine and Biology, Conference, 2021.
  • Tammewar A., Cervone A. and Riccardi G., " Emotion Carrier Recognition from Personal Narratives " Proc. INTERSPEECH, 2021.
  • Mousavi M., Cervone A., Danieli M. and Riccardi G., " Would you like to tell me more? Generating a corpus of psychotherapy dialogues " NAACL, Workshop on NLP for Medical Conversations, 2021.