Reft: Representation finetuning for language models
Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a
small number of weights. However, much prior interpretability work has shown that …
small number of weights. However, much prior interpretability work has shown that …
Philosophy of cognitive science in the age of deep learning
R Millière - Wiley Interdisciplinary Reviews: Cognitive Science, 2024 - Wiley Online Library
Deep learning has enabled major advances across most areas of artificial intelligence
research. This remarkable progress extends beyond mere engineering achievements and …
research. This remarkable progress extends beyond mere engineering achievements and …
Language models as models of language
R Millière - arXiv preprint arXiv:2408.07144, 2024 - arxiv.org
This chapter critically examines the potential contributions of modern language models to
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but
most prior work has focused on purely behavioural measures (eg, surprisal comparisons). At …
most prior work has focused on purely behavioural measures (eg, surprisal comparisons). At …
The limitations of large language models for understanding human language and cognition
C Cuskley, R Woods, M Flaherty - Open Mind, 2024 - direct.mit.edu
Researchers have recently argued that the capabilities of Large Language Models (LLMs)
can provide new insights into longstanding debates about the role of learning and/or …
can provide new insights into longstanding debates about the role of learning and/or …