When I play chess I filter out all kinds of illegal moves. I also filter out bad moves. Human is more like "recursively thinking of ideas and then evaluating them with another part of your model", why not let the LLMs do the same?
The internal model of a LLM is statistical text. Which is linear and fixed. Not great other than generating text similar to what was ingested.
Not at all. Like seriously, not in the slightest.
Their representation of the input is also not linear. Transformers use self-attention which relies on the softmax function, which is non-linear.
The internal model of a CPU is linear and fixed. Yet, a CPU can still generate an output which is very different from the input. It is not a simple lookup table, instead it executes complex algorithms.
An LLM has large amounts of input processing power. It has a large internal state. It executes "cycle by cycle", processing the inputs and internal state to generate output data and a new internal state.
So why shouldn't LLMs be capable of executing complex algorithms?
The issue is always inout consumption, and output correctness. In a CPU, we take great care with data representation and protocol definition, then we do formal verification on the algorithms, and we can be pretty sure that the output are correct. So the issue is that the internal model (for a given task) of LLMs are not consistent enough and the referential window (keeping track of each item in the system) is always too small.
> In a CPU, we take great care with data representation and protocol definition, then we do formal verification on the algorithms, and we can be pretty sure that the output are correct.
Sure, intelligent design makes for a better design in many ways.
That doesn't mean that an evolved design doesn't work at all, right?