The article discusses the unique aspects of human language and contrasts them with large language models (LLMs) like ChatGPT. Historically, language has been viewed as a defining trait of humanity, a perspective reinforced by thinkers like Aristotle. However, prominent linguist Noam Chomsky and colleagues argue that LLMs lack true inference capabilities and cannot engage with language in complex ways, as their knowledge stems purely from data.
Challenging this view, a study by linguists Gashpar Begush, Maximilian Dombkowski, and Ryan Rose examined various LLMs through rigorous language tests. While many models struggled with linguistic rules, one model stood out, demonstrating abilities comparable to linguistics graduate students, such as parsing sentences, resolving ambiguities, and handling complex structures like recursion. Begush noted that this finding challenges existing beliefs about AI capabilities.
Tom McCoy, a computational linguist, emphasized the importance of understanding LLM strengths and weaknesses, especially as society grows dependent on such technology. The researchers designed a four-part language test to discern genuine comprehension from mere data recall, utilizing tree diagrams to analyze sentence structures and explore recursion, showcasing the complexity inherent in human language.
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