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Comment by qnleigh

I might as well answer my own question, because I do think there are some coherent arguments for fundamental LLM limitations:

1. LLMs are trained on human-quality data, so they will naturally learn to mimic our limitations. Their capabilities should saturate at human or maybe above-average human performance.

2. LLMs do not learn from experience. They might perform as well as most humans on certain tasks, but a human who works in a certain field/code base etc. for long enough will internalize the relevant information more deeply than an LLM.

However I'm increasingly doubtful that these arguments are actually correct. Here are some counterarguments:

1. It may be more efficient to just learn correct logical reasoning, rather than to mimic every human foible. I stopped believing this argument when LLMs got a gold metal at the Math Olympiad.

2. LLMs alone may suffer from this limitation, but RL could change the story. People may find ways to add memory. Finally, it can't be ruled out that a very large, well-trained LLM could internalize new information as deeply as a human can. Maybe this is what's happening here:

https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...

parent qnleigh 7 months ago

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