35 citations · 59 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino +1
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature,…
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…
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Pierre Beckmann, Mikolaj Kegler, Milos Cernak
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech…