37 citations · 90 across the 11 of their papers we have counts for
13 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…
AI and Consciousness: Shifting Focus Towards Tractable Questions
Iulia-Maria Comsa
As language-based AI systems become more anthropomorphic, the question of whether they can have subjective experience is increasingly pressing. I focus here on the tractability of…
Does It Make Sense to Speak of Introspection in Large Language Models?
Iulia M. Comsa, Murray Shanahan
Large language models (LLMs) exhibit compelling linguistic behaviour, and sometimes offer self-reports, that is to say statements about their own nature, inner workings, or behavio…
Decoupling the components of geometric understanding in Vision Language Models
Eliza Kosoy, Annya Dahmani, Andrew K. Lampinen +4
Understanding geometry relies heavily on vision. In this work, we evaluate whether state-of-the-art vision language models (VLMs) can understand simple geometric concepts. We use a…
Can LLMs make trade-offs involving stipulated pain and pleasure states?
Geoff Keeling, Winnie Street, Martyna Stachaczyk +7
Pleasure and pain play an important role in human decision making by providing a common currency for resolving motivational conflicts. While Large Language Models (LLMs) can genera…
MiQA: A Benchmark for Inference on Metaphorical Questions
Iulia-Maria Comsa, Julian Martin Eisenschlos, Srini Narayanan
We propose a benchmark to assess the capability of large language models to reason with conventional metaphors. Our benchmark combines the previously isolated topics of metaphor de…