With llama.cpp, you can give it an ebnf grammar and constrain the output to any formal grammar you wish.
And how is the confidence score any different than the softmaxed token probabilities you get out of running every llm?
And how is the confidence score any different than the softmaxed token probabilities you get out of running every llm?
With llama-server, you can use basically any GGUF model as a Jev-like classifier. See Pi.dev's codemode and the accompanying llama backend to the "classify" function. The advantages of Jev-like inference are that:
- You generate 1 token per question in parallel, instead of sequentially generating a bunch of JSON punctuation. So you get much better latency and utilization.
- You can use a custom "sampler" that sees the raw logprobs for these tokens, including all the tokens that might have been generated. So you can generate some "probability" or "confidence" numbers based on how likely the model was to have generated a different answer.
So really, no difference? It's just a nice API with some computational efficiencies.
You're asking me how a structured output which assigns probabilities to classifications of the input text is different than the next-token weights which are calculated while generating that output?
One is internal (token prob), one is output generated by that internal mechanism e.g.,
"{ next_state: { "evade": 0.84, "land": 0.10, "search": 0.01"...}}"You have a ebnf grammar constrained to evade, land, search etc. Quite literally what's the difference between the log probs between those options and whatever number a very tiny model is coming up for the confidence.
Both use the softmax operation, but "probability of emitting a given label" is not the same as "probability the decided action is correct". OTOH, taking an action to sample something to come up with "probability of emitting a label" is in fact the whole shebang if the label is the next action. If it works it works.
Who cares, this distinction is small.
For me, a planning guy, either your suggestion or Jev-like would work just fine, and you better believe I'll be testing both as I'm quite excited about being able to use less-structured, untrained systems in decision pipelines. Thanks for the heads up.