So I never got the chance to play with LLMs because of the phone number requirement. But recently duckduckgo added a chat feature which lets you talk to these models so I have been trying them out.

I know these two models aren't state of the art but its tragic how they have devoured terabytes of corpus only to never have understood the meanings of the words they use.

I tried to talking to them about simple political issues. Once I asked why Elon Musk complains about woke while being a multibillionaire. I also asked why it is commonly said that Israel-Palestine conflict is complicated. Both times it gives very NYT-esque status quo-friendly answers which I think is very dangerous if the user is a naive person looking to delve into these topics. But as I question the premise and the assumptions of the answers, it immediately starts walking back and starts singing a completely different tune. Often I don't even have to explain the problems I have and just asking it to explain its assumptions is enough.

I got it from saying "Musk is critical of censorship and the lack of free speech" to "Musk is a billionaire in a highly unequal society and his societal criticisms are to be taken with a ton of salt". For the Palestine one, it started off with a list of reasons behind the complexity. Then I got it to strike them off one by one eventually concluding that the conflict is one of extreme power imbalance and that Palestinians are a clear victim of settler colonialism according to international consensus. My arguments weren't even that strong and it just caved in almost immediately.

I'm still trying to find use cases LLMs. Specifically I would be really happy if I could find a use for a small model like TinyLlama. I find that text summarization is promising but I wouldn't use it for a text I haven't read before because LLM is a liar sometimes.

  • KrasnaiaZvezda@lemmygrad.ml
    ·
    7 months ago

    New robots are also using LLMs both for understanding their enviroment with cameras, rather than complicated sensors that might not understand the world as we do, and for controlling movement by basically taking in the data from the robot and what other LLMs understand from the enviroment and predicting what inputs are needed to move correctly for movement or doing any tasks.

    As the LLMs get better they can also come up with better strategies too, which is already being used to some extent to have them create, test and fix codes based on output and error messages and this should soon allow fully autonomous robots as well that can think by themselves and interact with the world leading to many advancements, like full automation of work and scientific discoveries.