498 citations · 1.8k across the 31 of their papers we have counts for
33 papers
From cacophony to hierarchy: a principled framework for assessing AI consciousness
Shamil Chandaria, Arvo Muñoz Morán, Fernando Rosas +11
The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing theories th…
Schema-learning and rebinding as mechanisms of in-context learning and emergence
Sivaramakrishnan Swaminathan, Antoine Dedieu, Rajkumar Vasudeva Raju +3
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that u…
Role-Play with Large Language Models
Murray Shanahan, Kyle McDonell, Laria Reynolds
As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without fall…
Talking About Large Language Models
Murray Shanahan
Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this int…
Faithful Reasoning Using Large Language Models
Antonia Creswell, Murray Shanahan
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. Thi…
Sparse Relational Reasoning with Object-Centric Representations
Alex F. Spies, Alessandra Russo, Murray Shanahan
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsi…