4 citations · 9 across the 11 of their papers we have counts for
14 papers
Using profiles of cognitive capability to assess AI suitability for workplace tasks
Jonathan Prunty, Marko Tešić, Patrick Quinn +2
Organisations deploying AI face a scoping problem: which tasks can be automated, which should remain with humans, and which are best shared between the two. Aggregate benchmark sco…
Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
Ben Slater, Matteo G. Mecattaf, Lucy G. Cheke +2
Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autono…
Visuospatial Perspective Taking in Multimodal Language Models
Jonathan Prunty, Seraphina Zhang, Patrick Quinn +3
As multimodal language models (MLMs) are increasingly used in social and collaborative settings, it is crucial to evaluate their perspective-taking abilities. Existing benchmarks l…
I Spy With My Model's Eye: Visual Search as a Behavioural Test for MLLMs
John Burden, Jonathan Prunty, Ben Slater +3
Multimodal large language models (MLLMs) achieve strong performance on vision-language tasks, yet their visual processing is opaque. Most black-box evaluations measure task accurac…
Cognitive Science-Inspired Evaluation of Core Capabilities for Object Understanding in AI
Danaja Rutar, Alva Markelius, Konstantinos Voudouris +2
One of the core components of our world models is 'intuitive physics' - an understanding of objects, space, and causality. This capability enables us to predict events, plan action…
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed +23
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activitie…
Bringing Comparative Cognition To Computers
Konstantinos Voudouris, Lucy G. Cheke, Eric Schulz
Researchers are increasingly subjecting artificial intelligence systems to psychological testing. But to rigorously compare their cognitive capacities with humans and other animals…
Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers
Lorenzo Pacchiardi, Marko Tesic, Lucy G. Cheke +1
The integrity of AI benchmarks is fundamental to accurately assess the capabilities of AI systems. The internal validity of these benchmarks - i.e., making sure they are free from…
A little less conversation, a little more action, please: Investigating the physical common-sense of LLMs in a 3D embodied environment
Matteo G. Mecattaf, Ben Slater, Marko Tešić +3
As general-purpose tools, Large Language Models (LLMs) must often reason about everyday physical environments. In a question-and-answer capacity, understanding the interactions of…
100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances
Lorenzo Pacchiardi, Lucy G. Cheke, José Hernández-Orallo
Predicting the performance of LLMs on individual task instances is essential to ensure their reliability in high-stakes applications. To do so, a possibility is to evaluate the con…
The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
Konstantinos Voudouris, Ibrahim Alhas, Wout Schellaert +11
The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research commu…
Predictable Artificial Intelligence
Lexin Zhou, Pablo A. Moreno-Casares, Fernando Martínez-Plumed +12
We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key validity indicators (e.g., perfor…
Inferring Capabilities from Task Performance with Bayesian Triangulation
John Burden, Konstantinos Voudouris, Ryan Burnell +3
As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from d…
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition
Benjamin Beyret, José Hernández-Orallo, Lucy Cheke +3
Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, w…