Thanks veryone for the answers. Still hard to get my head around it. Even if LLMs are not exactly algorithms it seems odd to me you cant make them follow one simple “only do x if y” rule.
From my programming course in ~2005 the lego robots where all about those if sentences :/
The layman’s explanation of how an LLM works is it tries to predict the most likely word, or sequence of words, that follow from the last. This is based all on the input training set, which is compiled into a big bucket of probabilities. All text input influences those internal probabilities which in turn generates likely output. This is also why these things are error-prone because it’s really just hyper-sophisticated predictive text, and is doing its best to “play the odds.”
You can also view an LLM as one fiendishly massive if/else statement that chews on text tokens. There’s also some random seeding thrown in for more variation in output, but these things are 100% repeatable if you use the same seed every time; it’s just compiled logic.
I think a big thing that people are failing to understand is that most of these bits aren’t advanced LLMs that cost billions to develop, but bots that use existing LLMs. Therefore the programming on them isn’t super advanced and there will be workarounds.
Honestly the most effective way to keep them from getting tricked in the replies is to simply have them either not reply at all, or pre-program 50 or so standard prompts given to the LLM that are triggered by comment replies based on keywords.
Basically they need to filter the thread in such a way that the replies are never provided directly to the LLM.
Thanks veryone for the answers. Still hard to get my head around it. Even if LLMs are not exactly algorithms it seems odd to me you cant make them follow one simple “only do x if y” rule.
From my programming course in ~2005 the lego robots where all about those if sentences :/
The layman’s explanation of how an LLM works is it tries to predict the most likely word, or sequence of words, that follow from the last. This is based all on the input training set, which is compiled into a big bucket of probabilities. All text input influences those internal probabilities which in turn generates likely output. This is also why these things are error-prone because it’s really just hyper-sophisticated predictive text, and is doing its best to “play the odds.”
You can also view an LLM as one fiendishly massive if/else statement that chews on text tokens. There’s also some random seeding thrown in for more variation in output, but these things are 100% repeatable if you use the same seed every time; it’s just compiled logic.
Hehe best illustration. “big bucket of probabilities” …hell yeah
Yup. I had this in my head at the time:
I think a big thing that people are failing to understand is that most of these bits aren’t advanced LLMs that cost billions to develop, but bots that use existing LLMs. Therefore the programming on them isn’t super advanced and there will be workarounds.
Honestly the most effective way to keep them from getting tricked in the replies is to simply have them either not reply at all, or pre-program 50 or so standard prompts given to the LLM that are triggered by comment replies based on keywords.
Basically they need to filter the thread in such a way that the replies are never provided directly to the LLM.