565 citations · 647 across the 8 of their papers we have counts for
6 papers · 1 filter
Anthropocentric bias in language model evaluation
Raphaël Millière, Charles Rathkopf
Evaluating the cognitive capacities of large language models (LLMs) requires overcoming not only anthropomorphic but also anthropocentric biases. This article identifies two types…
LLMs as Models for Analogical Reasoning
Sam Musker, Alex Duchnowski, Raphaël Millière +1
Analogical reasoning -- the capacity to identify and map structural relationships between different domains -- is fundamental to human cognition and learning. Recent studies have s…
A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates
Raphaël Millière, Cameron Buckner
Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of huma…
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models
Safoora Yousefi, Leo Betthauser, Hosein Hasanbeig +2
Large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL) by leveraging task-specific examples in the input. However, the mechanisms…
The Vector Grounding Problem
Dimitri Coelho Mollo, Raphaël Millière
Large language models (LLMs) produce seemingly meaningful outputs, yet they are trained on text alone without direct interaction with the world. This leads to a modern variant of t…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…