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Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans
LLM performance in some language tasks presents both quantitative and qualitative differences from that of humans, however it remains to be determined whether such differences are amenable to model size.
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.
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.
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.
