1 citations · 1 across the 3 of their papers we have counts for
5 papers
Audit tools of the trade: Automated Auditor Elicitation Can Be Sensitive to the Context the Auditor Is Given
Ben Slater, Ryan Lundqvist, Leo Richter +1
Does providing additional context to automated AI auditors improve their effectiveness? Using the Petri tool to audit LLM coding agents, we test the impact of providing two types o…
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…
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…
PredictaBoard: Benchmarking LLM Score Predictability
Lorenzo Pacchiardi, Konstantinos Voudouris, Ben Slater +4
Despite possessing impressive skills, Large Language Models (LLMs) often fail unpredictably, demonstrating inconsistent success in even basic common sense reasoning tasks. This unp…
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…