I have been thinking about this and I have an idea for local LLM I want to try.
It is based on the assumption LLMs make mistakes (they will!) but be confident about it. Use a 1B model and ask it about a city for fun mistakes in an otherwise impressive array of facts. Then you will see how bullshitty LLMs are.
Anyway the idea is to constrain the LLM and a language understanding and to choose a constrained response.
For example give it the capability to read out something from my calendar. Using the logits it gets to choose what that calendar item is. But then regular old code does the lookup and writes a canned sentence saying "At 6pm today you have a meeting titled $title".
This way, my meeting schedule won't make my LLM talk like a pirate.
This massively culls what the LLM can do but what it does do, is like a search and so just like Google (before gen AI!) you get a result but you can judge it as a human.
It is based on the assumption LLMs make mistakes (they will!) but be confident about it. Use a 1B model and ask it about a city for fun mistakes in an otherwise impressive array of facts. Then you will see how bullshitty LLMs are.
Anyway the idea is to constrain the LLM and a language understanding and to choose a constrained response.
For example give it the capability to read out something from my calendar. Using the logits it gets to choose what that calendar item is. But then regular old code does the lookup and writes a canned sentence saying "At 6pm today you have a meeting titled $title".
This way, my meeting schedule won't make my LLM talk like a pirate.
This massively culls what the LLM can do but what it does do, is like a search and so just like Google (before gen AI!) you get a result but you can judge it as a human.