5 citations · 7 across the 4 of their papers we have counts for
6 papers
A Polyphonic Conception of AI Understanding
Matthieu Queloz, Pierre Beckmann
When a doctor, a judge, or an engineer must decide whether to trust an AI model's output, they cannot avoid asking what the model understands. Purely mathematical or statistical de…
World Modeling in Transformers
Pierre Beckmann, Matthieu Queloz, Andre Freitas
Behavioral failures can make a transformer appear to lack a world model even when it has learned faithful representations of its environment. We demonstrate this in TaxiGPT, a tran…
Why We Care About Understanding: Competence through Predictive Compression
Matthieu Queloz, Pierre Beckmann
What is the relation between understanding and compression, and why does human understanding take such a heavily compressed form? Across information theory, machine learning, and A…
Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence
Matthieu Queloz
This paper argues that explainability is only one facet of a broader ideal that shapes our expectations towards artificial intelligence (AI). Fundamentally, the issue is to what ex…
Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains
Matthieu Queloz
A key assumption fuelling optimism about the progress of large language models (LLMs) in accurately and comprehensively modelling the world is that the truth is systematic: true st…
Mechanistic Indicators of Understanding in Large Language Models
Pierre Beckmann, Matthieu Queloz
Large language models (LLMs) are often portrayed as merely imitating linguistic patterns without genuine understanding. We argue that recent findings in mechanistic interpretabilit…