Why Large Language Models (LLMs) Will Not Understand Human Language (2022) (jeremyhadfield.com)

Despite recent advances and hype surrounding large language models (LLMs) like GPT-3, this analysis argues that they fundamentally cannot achieve true human-level understanding of language. While LLMs excel at predicting and generating text based on statistical patterns learned from massive datasets, their abilities remain limited to pattern matching and memorization rather than genuine comprehension. Critically, their architecture—rooted in probabilistic modeling and deep neural networks—lacks the representational and cognitive structures necessary for grasping the deeper semantics, concepts, and communicative intent driving human language. Key technical insights highlight that LLMs operate by predicting the next word in a sequence using embeddings and transformer-based self-attention, trained on vast text corpora. However, performance benchmarks such as perplexity scores and tests like SuperGLUE and the Winograd schema challenge reveal significant gaps compared to human linguistic abilities. The models often leverage data memorization, fail on novel or context-dependent linguistic tasks, and lack the capacity for robust reasoning or inferencing essential to understanding. This gap is underscored by empirical studies demonstrating LLM susceptibility to spurious correlations and their inability to handle pragmatic or conceptual nuances. Ultimately, the piece calls for a recalibration of expectations around LLMs, emphasizing their dependence on human guidance and their role as sophisticated pattern recognition tools rather than true language understanders. It suggests that future progress will require novel approaches transcending brute-force scale and statistical learning, incorporating insights from cognitive science to model the systematic and compositional nature of language thought—paving a path toward machines that genuinely process meaning, not just simulate it.