Making Sense of Meaning: A Deep Dive into Large Language Models and the Complex Landscape of…
<p>Semantics — the study of meaning — is a profoundly complex topic that has challenged linguists, philosophers, and AI researchers for decades.</p>
<p>With the recent explosion in large language models, interest has surged in understanding these models’ capabilities and limitations when it comes to learning meaning.</p>
<p>Let’s untangle the web of meaning, evaluate the strengths and shortcomings of LLMs in semantic understanding, and take a look at the evolving strategies to make these models more human-like in their semantic grasp.</p>
<h2>I. What Exactly Constitutes Meaning?</h2>
<p>Breaking it down, linguists often categorize meaning into distinct types:</p>
<p><strong>Lexical Meaning:</strong> The straightforward definition of individual words. For instance, the word “table” signifies a flat-topped piece of furniture with legs.</p>
<p><strong>Compositional Meaning:</strong> This deals with how different words mesh together to give a sentence its unique meaning. Order matters; “The table is brown” and “the brown table” mean slightly different things.</p>
<p><strong>Pragmatic Meaning:</strong> Context is king here. The words in a sentence often carry nuances and implications that aren’t directly stated but understood through the given context.</p>
<p><strong>Associative Meaning</strong>: This is a more personal form of meaning. It involves the memories or feelings that a word might stir up in someone due to their unique experiences.</p>
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