498 citations · 1.8k across the 31 of their papers we have counts for
5 papers · 1 filter
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…
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Antonia Creswell, Murray Shanahan, Irina Higgins
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reas…
Abstraction for Deep Reinforcement Learning
Murray Shanahan, Melanie Mitchell
We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might b…