[xcv123]

LLMs can outperform humans on a variety of NLP tasks that require understanding. Formally, they are designed to solve "natural language understanding" tasks as a subset of "natural language processing" tasks. The word "understanding" is used in the academic context here. It is a standard term in NLP research.

https://en.wikipedia.org/wiki/Natural-language_understanding

My point was to show that their thinking, reasoning and language was flawed, that it lacked nuance and rigor. I am trying to raise the standards of discussion. They need to think more deeply about what "understanding" really means. Consciousness does not even have a formal universally agreed definition.

Sloppy non-rigorous shallow arguments are bad for discussion.

> LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.

That's a separate issue. They generally don't hallucinate when solving a problem within their context window. Recalling facts from their training set is another issue.

Humans sometimes have a similar problem of "hallucinating" when recalling facts from their long term memory.

[Guvante]

Except that if you narrow to a tiny training set you are back to problems that can be solved almost as quickly with full text search...

[xcv123]

Narrow to a tiny training set? What are you talking about now? That has nothing to do with deep learning.

GPT-3.5 was trained on at least 300 billion tokens. It has 96 layers in its neural network of 175 billion parameters. Each one of those 96 stacked layers has an attention mechanism that recomputes an attention score for every token in the context window, for each new token generated in sequence. GPT-4 is much bigger than that. The scale and complexity of these models is beyond comprehension. We're talking about LLMs, not SLMs.

[Guvante]

I misread context window as training set and thought you were switching to SLMs. My mistake.