It provides new evidence in a debate over the cognitive capacity of AI models, suggesting that artificial systems that reflect key aspects of human thinking may be possible.
“Understanding how humans conceptualise and categorise natural objects offers critical insights into perception and cognition,” the team said in a paper published in the peer-reviewed journal Nature Machine Intelligence on Tuesday.
“With the advent of large language models (LLMs), a key question arises: can these models develop humanlike object representations from linguistic and multimodal data?”
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LLMs are AI models trained on a vast amount of text data – along with visual and audio data in the case of multimodal large language models (MLLMs) – to process tasks.
