35 citations · 59 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Deep Learning Models Also Recall Features
Pierre Beckmann
Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual recall points to something br…
SciR: A Controllable Benchmark for Scientific Reasoning in LLMs
Pierre Beckmann, Marco Valentino, Andre Freitas
Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction. Reliably evaluating LLMs on these in scientific settings is cur…
Probing Persona-Dependent Preferences in Language Models
Oscar Gilg, Pierre Beckmann, Daniel Paleka +1
Large language models (LLMs) can be said to have preferences: they reliably pick certain tasks and outputs over others, and preferences shaped by post-training and prompting appear…
Where is the Mind? Persona Vectors and LLM Individuation
Pierre Beckmann, Patrick Butlin
The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach this problem through mechanistic in…