The article discusses the limitations of large language models (LLMs) in capturing human-like understanding. While they excel at categorization and prediction, they fail to grasp nuanced semantic distinctions vital for true comprehension. Borrowing from Rate-Distortion Theory, the authors highlight how LLMs, through their compression of language, discard elements that provide depth and ambiguity—qualities integral to human cognition. The work echoes findings from a recent Apple report, indicating a fundamental flaw in LLM reasoning, calling into question their representation of intelligence.
While LLMs form broad conceptual categories that align with human judgment, they struggle to capture the fine‑grained semantic distinctions crucial for human understanding.
But, LLMs do not think, they sort, predict, and compress. They don't understand.
In essence, the authors ask how much meaning LLMs lose as they compress human language into the efficient mathematical forms that are called embeddings.
The cost is the loss or reduction of ambiguity, depth, contradiction, and context.
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