The current generation of LLMs, such as GPT-4, excel in memorization-based tasks but struggle with common sense reasoning, often providing nonsensical answers to straightforward questions.
Multiple-choice questions used to measure machine common sense often fail to capture the complexity of human understanding, making it difficult to ascertain how close LLMs are to replicating human-like behavior.
Despite their impressive capabilities, LLMs fall short in their ability to interpret everyday contexts and navigate the uncertainties that humans manage effortlessly.
The aspiration for machines demonstrating 'artificial general intelligence' hinges on overcoming the limitations of LLMs in common sense understanding and contextual reasoning.
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