Mario Giulianelli
Associate Professor of Computational Linguistics · University College London
Director of the Computational Linguistics Postgraduate Programme · UCL PaLS
Member of the European Laboratory for Learning and Intelligent Systems · UCL ELLIS Unit

My research explores the information processing principles that underly the ability to use and learn language — both in human and artificial language processing systems. I'm also increasingly interested in biological and artificial cognitive systems more broadly, and by extension to extra-linguistic aspects of perception, action, and interaction. I am committed to advancing the science of AI evaluation, with a focus on language as well as broader aspects of agency and safety. In 2018, I co-authored a paper that, according to the very kind Aaron Mueller, introduced the first causal (mechanistic?) interpretability method for language models.

News
July 2026 Now hiring a Research Fellow in Machine Cognition at UCL

I am hiring a Research Fellow in Machine Cognition for a two-year position at UCL. The project will combine interpretability methods with behavioural evaluations to investigate how goals and beliefs are represented in language model agents, and whether these representations can be reliably extracted and manipulated. The position closes on 26 July 2026. Please feel free to get in touch if you are interested.

May 2026 Three papers presented at ICML 2026!

Three papers at ICML 2026: one combining behavioural and representational analyses to evaluate goal-directedness in LLM agents; one introducing a statistical framework for measuring bias and uncertainty in LLM-as-judge evaluations; and one showing that reasoning aligns the cognitive profiles of LLMs with those of humans (presented at the Mechanistic Interpretability and Combining Theory and Benchmarks workshops).

May 2026 Two papers presented at ACL 2026!

Two papers presented at ACL 2026 in San Diego. One paper, Probing for Reading Times, studies whether language model representations contain signatures of human reading times. The other, Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue, uses a new approach to generating open-ended goal-agnostic and goal-oriented alternatives to study speaker choice sensitivity to various information-theoretic notions of comprehension and production cost.

March 2026 Call for papers: LM Playschool Workshop and Challenge

We have announced the call for papers for the LM Playschool Workshop and Challenge. We invite submissions exploring the frontier of language agents that learn, adapt, and improve through situated interaction, with a focus on conversational, collaborative, goal-oriented, and multi-turn environments.

February 2026 Paper accepted at EACL 2026

Work with collaborators from Edinburgh on extending information-theoretic models of language production to visually grounded settings was accepted at EACL 2026.

Selected publications
2026
Raghu Arghal, Fade Chen, Niall Dalton, Evgenii Kortukov, Calum McNamara, Angelos Nalmpantis, Moksh Nirvaan, Gabriele Sarti, Mario Giulianelli
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
2026
Mario Giulianelli, Sarenne Wallbridge, Ryan Cotterell, Raquel Fernandez
Journal of Memory and Language
2025
Christopher Summerfield, Lennart Luettgau, Magda Dubois, Hannah Rose Kirk, Kobi Hackenburg, Catherine Fist, Katarina Slama, Nicola Ding, Rebecca Anselmetti, Andrew Strait, Mario Giulianelli, Cozmin Ududec
Preprint
2025
Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)
2024
Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, Brian DuSell, Tim Vieira, Ryan Cotterell
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)
2024
Mario Giulianelli, Andreas Opedal, Ryan Cotterell
Findings of the Association for Computational Linguistics: EMNLP 2024
2023
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, et al.
Nature Machine Intelligence 5, 1161-1174