73 citations · 210 across the 7 of their papers we have counts for
6 papers
Meaning without reference in large language models
Steven T. Piantadosi, Felix Hill
The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess…
Tell me why! Explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta +8
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of e…
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette
Adam Santoro, Felix Hill, David Barrett +3
Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial…
Embedding Word Similarity with Neural Machine Translation
Felix Hill, Kyunghyun Cho, Sebastien Jean +2
Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural mach…
Not All Neural Embeddings are Born Equal
Felix Hill, KyungHyun Cho, Sebastien Jean +2
Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation…
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
Felix Hill, Roi Reichart, Anna Korhonen
We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to g…