Meaning in Large Language Models: Form vs. Function (jgehring.net)

A recent discourse in AI explores the capabilities and limitations of large language models (LLMs) in terms of understanding and deriving meaning from text. While models like OpenAI's GPT series have achieved remarkable proficiency in language generation, the debate centers around whether these models can ever attain human-level intelligence. Advocates of LLMs cite their performance in generating coherent text and the potential for adapting them to various tasks through fine-tuning. In contrast, critics argue that these models fundamentally lack true understanding, claiming that their predictive nature does not equate to an intrinsic grasp of meaning. The significance of this debate lies in its implications for the future of AI development. At the philosophical level, understanding language is tied to complexities such as compositionality and meaning which have been extensively studied since the 1970s. Researchers are reassessing the technical foundations of LLMs, including whether architectures like Transformers can achieve Turing completeness and generalize beyond their training data. This conversation not only questions the effectiveness of LLMs in acquiring true semantic understanding but also challenges the AI community to confront the broader dimensions of machine cognition and its relation to human intelligence.