A prompt-tuned LLM with data augmentation and cultural context prompts reportedly recognizes multicultural names at 93.1% accuracy and unseen names at 89.5%, but the evidence is not reproducible.
The Debate Over Understanding in AI's Large Language Models
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abstract
We survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. We describe arguments that have been made for and against such understanding, and key questions for the broader sciences of intelligence that have arisen in light of these arguments. We contend that a new science of intelligence can be developed that will provide insight into distinct modes of understanding, their strengths and limitations, and the challenge of integrating diverse forms of cognition.
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Large Language Models for Zero-Shot Multicultural Name Recognition
A prompt-tuned LLM with data augmentation and cultural context prompts reportedly recognizes multicultural names at 93.1% accuracy and unseen names at 89.5%, but the evidence is not reproducible.