Birbla

Join the discussion

Write your take first — we'll ask for email only when you're ready to publish.

Sign in
  • Hacker News
  • I believe it depends on the LLM itself. Like what model as each model has diff weights and diff data trained onn
    by VaradD09
  • Kinda weird to generalize "LLM". Every lab, every model is different. Has its own biases, reward functions etc.
    by Tsarp
  • I kinda find it funny when I use the advisor on claude code and it agrees with the ideas that the previous model did.

    For info: the advisor(s) available are higher end models. For example: you use sonnet, the available advisors are opus and fable. If you use Haiku, the advisor are sonnet, opus and fable.

    by ex1fm3ta
  • They would all agree raspberry has two Rs
    by bryzaguy
  • Since the baseline LLM architecture is similar with each other, maybe already there are relations between their each opinion. Of course, this is an assumption and the explicit training seems effective in this case. But, I'm curious whether the approach would be effective in other cases (in terms of generalization?)
    by lhk931122
  • Observation: A panel of Judges (multiple Judges), whether it's multiple AI's or not, is fundamentally -- a Jury!
    by peter_d_sherman
  • While this is absolutely true - I'd hesitate to discount using similar agents for checking each other. Two agents will almost never hallucinate in the same way, regardless of their weights - and by having a second one (with a different context) check almost entirely eliminates the problem.
    by qarl
  • A shared blind spot as noted at the bottom needs to be considered more often. In my day job, most of my coordination with others and now LLMs, is clarifying context and requirements. Claude is very happy to make assertions without the full picture in my experience, even when I give it as much context as I can.
    by mey

Explore Birbla archives