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A Comparative Investigation of Compositional Syntax and Semantics in DALL-E 2
In this study we compared how well DALL-E 2 visually represented the meaning of linguistic prompts also given to young children in comprehension tests.
What is a word?
In order to design strong paradigms for isolating lexical access and semantics, we need to know what a word is.
The Quo Vadis of the Relationship between Language and Large Language Models
In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language.
A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Human Language?
Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction.
ROSE: A Neurocomputational Architecture for Syntax
ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.
Testing AI performance on less frequent aspects of language reveals insensitivity to underlying meaning
With successes in bottom-up challenges partially overshadowing shortcomings, the 'human-like' performance of Large Language Models has raised the question of how linguistic performance is achieved by algorithms.
Natural Language Syntax Complies with the Free-Energy Principle
Natural language syntax yields an unbounded array of hierarchically structured expressions.
DALL-E 2 Fails to Reliably Capture Common Syntactic Processes
Machine intelligence is increasingly being linked to claims about sentience, language processing, and an ability to comprehend and transform natural language into a range of stimuli.
