From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks
Compositionality has long been considered a key explanatory property underlying human
intelligence: arbitrary concepts can be composed into novel complex combinations …
intelligence: arbitrary concepts can be composed into novel complex combinations …
Toward Compositional Behavior in Neural Models: A Survey of Current Views
Compositionality is a core property of natural language, and compositional behavior (CB) is
a crucial goal for modern NLP systems. The research literature, however, includes …
a crucial goal for modern NLP systems. The research literature, however, includes …
Mechanisms of Symbol Processing for In-Context Learning in Transformer Networks
Large Language Models (LLMs) have demonstrated impressive abilities in symbol
processing through in-context learning (ICL). This success flies in the face of decades of …
processing through in-context learning (ICL). This success flies in the face of decades of …
Terminating Differentiable Tree Experts
We advance the recently proposed neuro-symbolic Differentiable Tree Machine, which
learns tree operations using a combination of transformers and Tensor Product …
learns tree operations using a combination of transformers and Tensor Product …
Special Session: Neuro-Symbolic Architecture Meets Large Language Models: A Memory-Centric Perspective
Large language models (LLMs) have significantly transformed the landscape of artificial
intelligence, demonstrating exceptional capabilities in natural language understanding and …
intelligence, demonstrating exceptional capabilities in natural language understanding and …
Discrete Dictionary-based Decomposition Layer for Structured Representation Learning
Neuro-symbolic neural networks have been extensively studied to integrate symbolic
operations with neural networks, thereby improving systematic generalization. Specifically …
operations with neural networks, thereby improving systematic generalization. Specifically …
Rules, frequency, and predictability in morphological generalization: behavioral and computational evidence from the German plural system
K McCurdy, K McCurdy - 2024 - era.ed.ac.uk
Morphological generalization, or the task of mapping an unknown word (such as a novel
noun Raun) to an inflected form (such as the plural Rauns), has historically proven a …
noun Raun) to an inflected form (such as the plural Rauns), has historically proven a …
Recurrent Transformers Trade-off Parallelism for Length Generalization on Regular Languages
Transformers have achieved remarkable success in Natural Language Processing but
struggle with state tracking and algorithmic reasoning tasks, such as modeling Regular …
struggle with state tracking and algorithmic reasoning tasks, such as modeling Regular …